Bioluminescence imaging is one of the most widely used imaging techniques in the study of infectious diseases (N. Andreu et al., 2011). Luciferases have been used in mycobacterial research for more than 20 years; the two most widely used are the firefly luciferase (FFluc) and the luciferase of the bacterium *Vibrio harveyi* (LuxAB). Both luciferases produce light in the presence of a combination of a substrate and a cofactor, namely D-luciferin and ATP (for FFluc) and n-decanal and FMNH2 (for LuxAB). As the co-factors are only found in live cells, the production of light by the luciferases provides a sensitive indicator of cell viability. The bacterial luciferase system has a major advantage when compared with the FFluc: the genes for the synthesis of the substrate are known and can be co-expressed with the *luxAB* genes as a convenient gene set (*luxCABDE*) that renders the bacteria autoluminescent, that is, no external addition of substrate is needed for light production. Light-emitting mycobacteria have been used as an easier and faster approach than commonly used methods to assess bacterial numbers *in vitro* and in macrophages, for example, in drug screening assays (Arain et al., 1996). The first approach to use luminescent mycobacteria in animal models consisted of measuring luminescence *ex vivo* in organ homogenates (Hickey et al., 1996). This method generated results in a much quicker time frame than using CFU counts and has been

applied to drug and vaccine efficacy testing (Hickey et al., 1996; Snewin et al., 1999).

More recently, a recombinant *M. bovis* BCG strain expressing the bacterial luciferase enzyme LuxAB has been used to monitor mycobacterial infection *in vivo* (Heuts et al., 2009). In this work, only the luciferase genes were expressed and, therefore, the n-decanal substrate had to be injected before imaging. Although n-decanal is very toxic, the authors were able to deliver it dissolved in a mixture of olive oil and ethanol by injection into the mouse peritoneum. To assess the usefulness of the system, immunodeficient RAG2−/−/cR−/− mice were intravenously infected with the luminescent BCG strain, and bioluminescence imaging was performed at different time points for 11 weeks. A signal coming from the spleen was detected four weeks post-infection, when the bacterial load was around 5x107 CFUs. The signal increased over time and extended to the abdomen of the animal but no signal was observed in the lungs, even though CFU counting showed a bacterial burden in this organ of 107 CFUs at eight weeks post-infection. However, luminescence was detected in the excised lungs, suggesting that tissue attenuation was responsible for the failure to detect the signal in the whole animal. The same luminescent BCG strain was also used to assess drug efficacy and the host immune response. A reduction in light emission, which paralleled the reduction in bacterial numbers, was observed in treated mice compared to the untreated mice, as well as in inmunocompetent BALB/c and T-cell reconstituted RAG2−/−/cR−/− mice compared to immunosupressed RAG2−/−/cR−/− mice. Therefore, bioluminescence imaging allows monitoring of mycobacterial infection in mice. However, the system was not useful for imaging infection in the lungs, and a toxic substrate had to be administered to the mice

metabolize or excrete FIAU or its iodinated derivatives.

**3.5 Biophotonic Imaging (BPI)**

before imaging.

PET/CT imaging is also being used in non-human primates, although the results have only been presented in meetings and no peer-reviewed article has been published to date. For example, PET/CT has been used to monitor disease progression and drug efficacy in macaques (Lin et al., 2009). Using CT imaging, lesions as small as 1 mm were detected in the lungs and lymph nodes of infected animals. Moreover, lesion progression could be followed over time. Interestingly, co-registered [18F]-FDG–PET images revealed that individual granulomas differed in their [18F]-FDG affinity: whereas some granulomas exhibited high uptake values, others seemed devoid of [18F]-FDG. The imaging results were complemented with post-mortem histology and bacterial burden analysis of individual lesions. The authors found a complex, lesion-specific response to drug treatment that included changes in [18F]- FDG avidity. These remarkable results show that even though PET and CT are two complementary techniques, images should be first analysed separately, and that caution should be taken when interpreting the results of PET activity in terms of [18F]-FDG accumulation.

#### **3.4 Single Photon Emission Computed Tomography (SPECT)**

SPECT/CT has also been used for imaging of TB infection in mice (Davis et al., 2009a). The authors used the radiotracer 1-(2′deoxy-2′-fluoro-β-D-arabinofuranosyl)-5-[125I]-iodouracil ([125I]-FIAU), a nucleoside analogue, together with an engineered *M. tuberculosis* strain that stably expressed the enzyme thymidine kinase (TK) which phosphorylates [125I]-FIAU leading to its accumulation within the bacteria. In contrast to [18F]-FDG–PET imaging, this technique specifically images the bacteria instead of the inflammatory response, as [125I]- FIAU is a poor substrate for mammalian TK. Using this technique, the authors were able to image individual necrotic granulomas in the lungs of C3HeB/FeJ infected mice (Figure 3). The presence of the lesions was subsequently corroborated by histopathology. However, the limit of detection was found to be 5x106 to 1x107 CFUs, a rather high bacterial burden for mice infected with *M. tuberculosis*. The authors suggested that the sensitivity of the method could be improved by increasing the expression of TK in the bacilli or by using more

Fig. 3. Co-registered SPECT and CT images from a live C3HeB/FeJ mouse infected with a low-dose aerosol of an *M. tuberculosis* strain expressing bacterial thymidine kinase (TK) under the control of a strong mycobacterial promoter. TB lesions were imaged 8-weeks after this infection, using [125I]-FIAU, a nucleoside analog substrate for bacterial TK*.* The FIAU-SPECT signal localizes to the TB lesion (crosshairs) in the lungs, indicating uptake of FIAU by the bacteria (Davis, S.L. & Jain, S.K.; unpublished data).

sensitive (and expensive) radionuclides such as 123I or 124I. Other limitations of the technique include: the limited blood supply at the centre of the granulomas could limit accessibility to imaging substrates; TK requires ATP, which could be restricted in latent bacteria; and the presence of non-specific signal in tissues such as liver, gall bladder, or stomach, that either metabolize or excrete FIAU or its iodinated derivatives.

#### **3.5 Biophotonic Imaging (BPI)**

318 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

PET/CT imaging is also being used in non-human primates, although the results have only been presented in meetings and no peer-reviewed article has been published to date. For example, PET/CT has been used to monitor disease progression and drug efficacy in macaques (Lin et al., 2009). Using CT imaging, lesions as small as 1 mm were detected in the lungs and lymph nodes of infected animals. Moreover, lesion progression could be followed over time. Interestingly, co-registered [18F]-FDG–PET images revealed that individual granulomas differed in their [18F]-FDG affinity: whereas some granulomas exhibited high uptake values, others seemed devoid of [18F]-FDG. The imaging results were complemented with post-mortem histology and bacterial burden analysis of individual lesions. The authors found a complex, lesion-specific response to drug treatment that included changes in [18F]- FDG avidity. These remarkable results show that even though PET and CT are two complementary techniques, images should be first analysed separately, and that caution should be taken when interpreting the results of PET activity in terms of [18F]-FDG

SPECT/CT has also been used for imaging of TB infection in mice (Davis et al., 2009a). The authors used the radiotracer 1-(2′deoxy-2′-fluoro-β-D-arabinofuranosyl)-5-[125I]-iodouracil ([125I]-FIAU), a nucleoside analogue, together with an engineered *M. tuberculosis* strain that stably expressed the enzyme thymidine kinase (TK) which phosphorylates [125I]-FIAU leading to its accumulation within the bacteria. In contrast to [18F]-FDG–PET imaging, this technique specifically images the bacteria instead of the inflammatory response, as [125I]- FIAU is a poor substrate for mammalian TK. Using this technique, the authors were able to image individual necrotic granulomas in the lungs of C3HeB/FeJ infected mice (Figure 3). The presence of the lesions was subsequently corroborated by histopathology. However, the limit of detection was found to be 5x106 to 1x107 CFUs, a rather high bacterial burden for mice infected with *M. tuberculosis*. The authors suggested that the sensitivity of the method could be improved by increasing the expression of TK in the bacilli or by using more

Fig. 3. Co-registered SPECT and CT images from a live C3HeB/FeJ mouse infected with a low-dose aerosol of an *M. tuberculosis* strain expressing bacterial thymidine kinase (TK) under the control of a strong mycobacterial promoter. TB lesions were imaged 8-weeks after this infection, using [125I]-FIAU, a nucleoside analog substrate for bacterial TK*.* The FIAU-SPECT signal localizes to the TB lesion (crosshairs) in the lungs, indicating uptake of FIAU

by the bacteria (Davis, S.L. & Jain, S.K.; unpublished data).

**3.4 Single Photon Emission Computed Tomography (SPECT)**

accumulation.

Bioluminescence imaging is one of the most widely used imaging techniques in the study of infectious diseases (N. Andreu et al., 2011). Luciferases have been used in mycobacterial research for more than 20 years; the two most widely used are the firefly luciferase (FFluc) and the luciferase of the bacterium *Vibrio harveyi* (LuxAB). Both luciferases produce light in the presence of a combination of a substrate and a cofactor, namely D-luciferin and ATP (for FFluc) and n-decanal and FMNH2 (for LuxAB). As the co-factors are only found in live cells, the production of light by the luciferases provides a sensitive indicator of cell viability. The bacterial luciferase system has a major advantage when compared with the FFluc: the genes for the synthesis of the substrate are known and can be co-expressed with the *luxAB* genes as a convenient gene set (*luxCABDE*) that renders the bacteria autoluminescent, that is, no external addition of substrate is needed for light production. Light-emitting mycobacteria have been used as an easier and faster approach than commonly used methods to assess bacterial numbers *in vitro* and in macrophages, for example, in drug screening assays (Arain et al., 1996). The first approach to use luminescent mycobacteria in animal models consisted of measuring luminescence *ex vivo* in organ homogenates (Hickey et al., 1996). This method generated results in a much quicker time frame than using CFU counts and has been applied to drug and vaccine efficacy testing (Hickey et al., 1996; Snewin et al., 1999).

More recently, a recombinant *M. bovis* BCG strain expressing the bacterial luciferase enzyme LuxAB has been used to monitor mycobacterial infection *in vivo* (Heuts et al., 2009). In this work, only the luciferase genes were expressed and, therefore, the n-decanal substrate had to be injected before imaging. Although n-decanal is very toxic, the authors were able to deliver it dissolved in a mixture of olive oil and ethanol by injection into the mouse peritoneum. To assess the usefulness of the system, immunodeficient RAG2−/−/cR−/− mice were intravenously infected with the luminescent BCG strain, and bioluminescence imaging was performed at different time points for 11 weeks. A signal coming from the spleen was detected four weeks post-infection, when the bacterial load was around 5x107 CFUs. The signal increased over time and extended to the abdomen of the animal but no signal was observed in the lungs, even though CFU counting showed a bacterial burden in this organ of 107 CFUs at eight weeks post-infection. However, luminescence was detected in the excised lungs, suggesting that tissue attenuation was responsible for the failure to detect the signal in the whole animal. The same luminescent BCG strain was also used to assess drug efficacy and the host immune response. A reduction in light emission, which paralleled the reduction in bacterial numbers, was observed in treated mice compared to the untreated mice, as well as in inmunocompetent BALB/c and T-cell reconstituted RAG2−/−/cR−/− mice compared to immunosupressed RAG2−/−/cR−/− mice. Therefore, bioluminescence imaging allows monitoring of mycobacterial infection in mice. However, the system was not useful for imaging infection in the lungs, and a toxic substrate had to be administered to the mice before imaging.

Molecular Imaging in TB: From the Bench to the Clinic 321

when travelling through tissues. This is exemplified by the finding that as few as 105 CFUs of a BCG strain that expressed tdTomato (excitation 554 nm, emission 581 nm) can be detected after being subcutaneously injected into mice, in comparison to 107 CFUs of BCG expressing enhanced GFP (EGFP) (excitation 484 nm, emission 510 nm) (Kong et al., 2009). Therefore, far-red reporters show a lot of promise for fluorescence *in vivo* imaging of *M. tuberculosis* infection in animal models, and the expression of several red FPs in

The strategies presented above use recombinant bacteria that express an exogenous FP. A much more versatile strategy consists of using an activatable fluorescent agent that is nonfluorescent in its native (quenched) state but produces fluorescence through enzymemediated release of its fluorochrome. This strategy has been widely used in cancer and inflammation research but, until recently, not in infectious diseases. Using a near-infrared fluorogenic substrate for -lactamase, an enzyme that is endogenously expressed by *M. tuberculosis* but not by eukaryotic cells, it is possible to detect 106 CFUs of *M. tuberculosis* subcutaneously injected in mice (Kong et al., 2010). The maximal signal was produced 48 h after substrate injection, and no signal was detected 48 h later, which suggests that repetitive imaging of the same animals can be done every 96 h. Surprisingly, the limit of the detection in the lungs of live animals was 104 CFUs, which is far lower than the limit of detection subcutaneously, even though the lungs are localised much deeper in the body. The signal was localised laterally, close to the armpit of the animal, and 3D fluorescence molecular tomography (FMT) and imaging of the excised lungs proved that the lungs were the source of the signal. The amount of fluorescence correlated with bacterial numbers when the imaging was performed 24 h post-substrate administration, but at later time-points substrate accumulation lead to a similar level of fluorescence independently of bacterial numbers. In addition, the technique was used to assess drug efficacy by imaging treated and untreated mice, showing that the signal increased in the untreated group while it decreased in the treated group. Although much work needs to be done to assess the usefulness of the technique, one can imagine many potential applications not only in *in vivo* imaging but also *in vitro* using fluorescence microscropy or FACS, as well as for TB diagnosis (e.g. detection of bacilli in sputum or imaging tuberculosis in patients). However, a limitation that needs to be considered is the fact that other bacteria, such as *Pseudomonas aeruginosa* or *Staphylococcus aureus*, also express -lactamase and therefore may give a false signal. Thus, alternative fluorogenic substrates that are activated by other endogenous enzymes are currently under study; for example, certain trehalose analogues that are substrates for the *M. tuberculosis*

mycobacteria has recently been optimised (Carroll et al., 2010).

mycolyltransesterases Ag85A, Ag85B and Ag85C (Backus et al., 2011).

The plain radiograph was first described in 1895 by Röntgen, at the same time that the TB pandemic was peaking in wealthy Western countries such as Victorian England. Consequently, there is a long experience and literature of plain radiographic imaging of TB, which has been reviewed previously (McAdams et al., 1995; J. Andreu et al., 2004; Curvo-Semedo et al., 2005). In summary, TB can cause a wide array of chest x-ray appearances, but classically causes consolidation and cavitation in the apices of the upper lobes. TB can also cause disease at the apices of the lower lobes, which appears in the mid-zone on chest

**4. Imaging TB in human disease**

**4.1 Radiography**

To overcome these difficulties, our group has recently optimised the expression of FFluc and the complete bacterial luciferase system in *M. tuberculosis* (N. Andreu et al., 2010). The resulting mycobacterial strains express either the optimised gene encoding FFluc (which is the brightest luciferase and uses a non-toxic substrate) or the optimised *luxCABDE* gene set from *Photorhabdus luminescens* (which results in autoluminescent strains that do not need the exogenous addition of substrate to produce light). Both *M. tuberculosis* strains were imaged *in vivo* in the lungs and spleens of infected mice (Figure 4), with limits of detection of around 105-106 CFU per lung and 105 CFU per spleen, whereas as few as 104 CFU can be imaged in the dissected organs (our unpublished results). Further work will assess the usefulness of these luminescent mycobacteria in drug efficacy testing and in other small animal models such as guinea pigs.

Fig. 4. Visualisation of bioluminescent bacteria within living mice infected with *FFluc*expressing *M. tuberculosis* after administration of luciferin*.* The image was obtained using an IVIS Spectrum and is displayed as a pseudocolour image, where red represents the most intense light emission while blue correspond to the weakest signal (Andreu, N. & Wiles, S; unpublished data).

Fluorescence imaging has had a more limited use in the study of infectious diseases, although it has been widely applied to other research fields such as cancer research (N. Andreu et al., 2011). In a first attempt to develop fluorescence imaging of *M. tuberculosis* infection, a GFP-expressing *M. tuberculosis* strain was used to infect mice and guinea pigs, and five weeks post-infection the lungs were imaged using a photon imager (Sugawara et al., 2006). Granulomas as small as 1 mm of diameter were detected, and the results were corroborated by histopathology examination. The same fluorescence technique was used to visualize granulomas in a latent model of TB in guinea pigs (Sugawara et al., 2009). In this case, the animals were subcutaneously infected with *M. tuberculosis* and the infection was followed for 10 months. No clinical signs of infection were evident in any of the animals for the length of the experiment, although the bacteriological analysis of lungs and spleens 180 and 300 days post-infection showed the presence of a few bacteria. Similarly, even though no granulomas were detected by gross pathology examination, microgranulomas were observed in the histological analysis. According to the authors, these small lesions corresponded to the fluorescent spots detected by photon imaging of sliced lungs and spleens. More work is still needed to validate these results and to be able to use this technology *in vivo*.

FPs that emit light in the far-red region of the spectrum are more appropriate for *in vivo* imaging than for example GFP, as red light is less affected by absorption and scattering

To overcome these difficulties, our group has recently optimised the expression of FFluc and the complete bacterial luciferase system in *M. tuberculosis* (N. Andreu et al., 2010). The resulting mycobacterial strains express either the optimised gene encoding FFluc (which is the brightest luciferase and uses a non-toxic substrate) or the optimised *luxCABDE* gene set from *Photorhabdus luminescens* (which results in autoluminescent strains that do not need the exogenous addition of substrate to produce light). Both *M. tuberculosis* strains were imaged *in vivo* in the lungs and spleens of infected mice (Figure 4), with limits of detection of around 105-106 CFU per lung and 105 CFU per spleen, whereas as few as 104 CFU can be imaged in the dissected organs (our unpublished results). Further work will assess the usefulness of these luminescent mycobacteria in drug efficacy testing and in other small animal models

Fig. 4. Visualisation of bioluminescent bacteria within living mice infected with *FFluc*expressing *M. tuberculosis* after administration of luciferin*.* The image was obtained using an IVIS Spectrum and is displayed as a pseudocolour image, where red represents the most intense light emission while blue correspond to the weakest signal (Andreu, N. & Wiles, S;

Fluorescence imaging has had a more limited use in the study of infectious diseases, although it has been widely applied to other research fields such as cancer research (N. Andreu et al., 2011). In a first attempt to develop fluorescence imaging of *M. tuberculosis* infection, a GFP-expressing *M. tuberculosis* strain was used to infect mice and guinea pigs, and five weeks post-infection the lungs were imaged using a photon imager (Sugawara et al., 2006). Granulomas as small as 1 mm of diameter were detected, and the results were corroborated by histopathology examination. The same fluorescence technique was used to visualize granulomas in a latent model of TB in guinea pigs (Sugawara et al., 2009). In this case, the animals were subcutaneously infected with *M. tuberculosis* and the infection was followed for 10 months. No clinical signs of infection were evident in any of the animals for the length of the experiment, although the bacteriological analysis of lungs and spleens 180 and 300 days post-infection showed the presence of a few bacteria. Similarly, even though no granulomas were detected by gross pathology examination, microgranulomas were observed in the histological analysis. According to the authors, these small lesions corresponded to the fluorescent spots detected by photon imaging of sliced lungs and spleens. More work is still needed to validate these results and to be able to use this

FPs that emit light in the far-red region of the spectrum are more appropriate for *in vivo* imaging than for example GFP, as red light is less affected by absorption and scattering

such as guinea pigs.

unpublished data).

technology *in vivo*.

when travelling through tissues. This is exemplified by the finding that as few as 105 CFUs of a BCG strain that expressed tdTomato (excitation 554 nm, emission 581 nm) can be detected after being subcutaneously injected into mice, in comparison to 107 CFUs of BCG expressing enhanced GFP (EGFP) (excitation 484 nm, emission 510 nm) (Kong et al., 2009). Therefore, far-red reporters show a lot of promise for fluorescence *in vivo* imaging of *M. tuberculosis* infection in animal models, and the expression of several red FPs in mycobacteria has recently been optimised (Carroll et al., 2010).

The strategies presented above use recombinant bacteria that express an exogenous FP. A much more versatile strategy consists of using an activatable fluorescent agent that is nonfluorescent in its native (quenched) state but produces fluorescence through enzymemediated release of its fluorochrome. This strategy has been widely used in cancer and inflammation research but, until recently, not in infectious diseases. Using a near-infrared fluorogenic substrate for -lactamase, an enzyme that is endogenously expressed by *M. tuberculosis* but not by eukaryotic cells, it is possible to detect 106 CFUs of *M. tuberculosis* subcutaneously injected in mice (Kong et al., 2010). The maximal signal was produced 48 h after substrate injection, and no signal was detected 48 h later, which suggests that repetitive imaging of the same animals can be done every 96 h. Surprisingly, the limit of the detection in the lungs of live animals was 104 CFUs, which is far lower than the limit of detection subcutaneously, even though the lungs are localised much deeper in the body. The signal was localised laterally, close to the armpit of the animal, and 3D fluorescence molecular tomography (FMT) and imaging of the excised lungs proved that the lungs were the source of the signal. The amount of fluorescence correlated with bacterial numbers when the imaging was performed 24 h post-substrate administration, but at later time-points substrate accumulation lead to a similar level of fluorescence independently of bacterial numbers. In addition, the technique was used to assess drug efficacy by imaging treated and untreated mice, showing that the signal increased in the untreated group while it decreased in the treated group. Although much work needs to be done to assess the usefulness of the technique, one can imagine many potential applications not only in *in vivo* imaging but also *in vitro* using fluorescence microscropy or FACS, as well as for TB diagnosis (e.g. detection of bacilli in sputum or imaging tuberculosis in patients). However, a limitation that needs to be considered is the fact that other bacteria, such as *Pseudomonas aeruginosa* or *Staphylococcus aureus*, also express -lactamase and therefore may give a false signal. Thus, alternative fluorogenic substrates that are activated by other endogenous enzymes are currently under study; for example, certain trehalose analogues that are substrates for the *M. tuberculosis* mycolyltransesterases Ag85A, Ag85B and Ag85C (Backus et al., 2011).

#### **4. Imaging TB in human disease**

#### **4.1 Radiography**

The plain radiograph was first described in 1895 by Röntgen, at the same time that the TB pandemic was peaking in wealthy Western countries such as Victorian England. Consequently, there is a long experience and literature of plain radiographic imaging of TB, which has been reviewed previously (McAdams et al., 1995; J. Andreu et al., 2004; Curvo-Semedo et al., 2005). In summary, TB can cause a wide array of chest x-ray appearances, but classically causes consolidation and cavitation in the apices of the upper lobes. TB can also cause disease at the apices of the lower lobes, which appears in the mid-zone on chest

Molecular Imaging in TB: From the Bench to the Clinic 323

radiography, but does not give information about the molecular events occurring at the site

Fig. 6. CT imaging reveals a small left mid zone cavity (indicated by arrow) that was not visible on plain chest x-ray (left panel) and a tree-in-bud bronchial filling in a 33 year old man with pulmonary TB (right panel), which appears as branched opacities in the lung field

MRI provides the best imaging of the meninges and spinal cord, and so is useful in the diagnosis of cerebral TB, TB meningitis and paraspinal TB abscesses. MRI is a rapidly developing area, and so may emerge as a modality which can provide insight into molecular events in TB. The advantage of MRI is that it involves no ionizing radiation and provides excellent anatomical resolution. New MRI modalities to investigate inflammatory diseases are under development (Pirko et al., 2004), but these have not yet entered the clinical arena

PET imaging is a widely used nuclear medicine technique which has the potential to study pathological events at a molecular level before extensive anatomical changes are observed on plain radiography. PET imaging is commonly combined with CT scanning in patients to provide both functional and anatomical information. [18F]-FDG accumulates in metabolically active cells after phosphorylation, and so is taken up by metabolically active macrophages within the TB granuloma and other inflammatory foci. A primary limitation of PET imaging for TB is the high cost and low availability in developing world. Increased PET uptake is well described in both pulmonary and extrapulmonary TB lesions (Matsuura et al., 2000; Bakheet et al., 1998; C.M. Yang et al., 2003), and can cause diagnostic uncertainty with

PET scanning is clinically useful in certain patients with TB. For example, when patients have normal radiology but symptoms highly suggestive of active TB, PET scans may identify occult foci of infection which can then be sampled to confirm the diagnosis and for culture (Figure 7, arrow). Furthermore, PET imaging has been proposed for monitoring the resolution of TB disease (Hofmeyr et al., 2007), although the benefit must be weighed against the increased radiation exposure. Current research questions which need to be

of infection.

adjacent to arrowhead.

for investigation in TB.

**4.3 Magnetic Resonance Imaging (MRI)**

**4.4 Positron Emission Tomography (PET)**

malignancy and other infections (Chen et al., 2004; Li et al., 2008).

radiograph (Figure 5). However, TB can result in a wide range of other features, such as miliary disease with small millet seed-sized nodules throughout the lungs, pleural effusions, mediastinal lymphadenopathy and extrapulmonary disease. In the era of HIV infection, where the host immune system is compromised, the appearances of pulmonary TB are often atypical (Kwan & Ernst, 2011), ranging from classical cavitation to areas of pneumonia to a normal chest x-ray even in the presence of a high mycobacterial load. This illustrates the importance of the adaptive immune response in driving lung inflammation, resulting in consolidation and tissue destruction.

Fig. 5. Radiograph illustrating right mid zone cavitation on a 17 year old patient with pulmonary TB.

As TB is treated, areas of consolidation tend to gradually resolve, leaving an area of fibrosis or scar tissue which persists for life. Cavities remain even after cure, because the lung cannot reconstruct the intricate extracellular matrix after it has been destroyed. However, it is well recognised that radiographic appearances may often worsen before they improve (Leung, 1999), and similarly some lesions may increase in size and density while others appear to resolve. Even in HIV negative patients with drug-sensitive disease, such "paradoxical" reactions may occur (Cheng, 2002). This demonstrates the different behaviour of inflammatory lesions even in the same patient, and one challenge for modern imaging techniques is to define the molecular mechanisms underlying this immune response to improve our understanding of what constitutes an effective as opposed to deleterious immune response to TB.

#### **4.2 Computed Tomography (CT)**

CT involves cross-sectional imaging of patients and so permits a much greater degree of resolution of anatomical structures, although it results in a higher radiation dose and higher cost than plain radiography. CT can demonstrate cavity formation with much greater sensitivity and will demonstrate subtle changes which may be missed on plain chest x-rays (Figure 6 [left panel]). For example, filling of small airways with inflammatory debris may result in a "tree-in-bud" pattern (Figure 6 [right panel], arrow), which should immediately alert the physician to the possibility of mycobacterial infection. Therefore, CT scanning provides information of changes at a much more precise anatomical level than plain

radiograph (Figure 5). However, TB can result in a wide range of other features, such as miliary disease with small millet seed-sized nodules throughout the lungs, pleural effusions, mediastinal lymphadenopathy and extrapulmonary disease. In the era of HIV infection, where the host immune system is compromised, the appearances of pulmonary TB are often atypical (Kwan & Ernst, 2011), ranging from classical cavitation to areas of pneumonia to a normal chest x-ray even in the presence of a high mycobacterial load. This illustrates the importance of the adaptive immune response in driving lung inflammation, resulting in

Fig. 5. Radiograph illustrating right mid zone cavitation on a 17 year old patient with

As TB is treated, areas of consolidation tend to gradually resolve, leaving an area of fibrosis or scar tissue which persists for life. Cavities remain even after cure, because the lung cannot reconstruct the intricate extracellular matrix after it has been destroyed. However, it is well recognised that radiographic appearances may often worsen before they improve (Leung, 1999), and similarly some lesions may increase in size and density while others appear to resolve. Even in HIV negative patients with drug-sensitive disease, such "paradoxical" reactions may occur (Cheng, 2002). This demonstrates the different behaviour of inflammatory lesions even in the same patient, and one challenge for modern imaging techniques is to define the molecular mechanisms underlying this immune response to improve our understanding of what constitutes an effective as opposed to deleterious

CT involves cross-sectional imaging of patients and so permits a much greater degree of resolution of anatomical structures, although it results in a higher radiation dose and higher cost than plain radiography. CT can demonstrate cavity formation with much greater sensitivity and will demonstrate subtle changes which may be missed on plain chest x-rays (Figure 6 [left panel]). For example, filling of small airways with inflammatory debris may result in a "tree-in-bud" pattern (Figure 6 [right panel], arrow), which should immediately alert the physician to the possibility of mycobacterial infection. Therefore, CT scanning provides information of changes at a much more precise anatomical level than plain

consolidation and tissue destruction.

pulmonary TB.

immune response to TB.

**4.2 Computed Tomography (CT)**

radiography, but does not give information about the molecular events occurring at the site of infection.

Fig. 6. CT imaging reveals a small left mid zone cavity (indicated by arrow) that was not visible on plain chest x-ray (left panel) and a tree-in-bud bronchial filling in a 33 year old man with pulmonary TB (right panel), which appears as branched opacities in the lung field adjacent to arrowhead.

#### **4.3 Magnetic Resonance Imaging (MRI)**

MRI provides the best imaging of the meninges and spinal cord, and so is useful in the diagnosis of cerebral TB, TB meningitis and paraspinal TB abscesses. MRI is a rapidly developing area, and so may emerge as a modality which can provide insight into molecular events in TB. The advantage of MRI is that it involves no ionizing radiation and provides excellent anatomical resolution. New MRI modalities to investigate inflammatory diseases are under development (Pirko et al., 2004), but these have not yet entered the clinical arena for investigation in TB.

#### **4.4 Positron Emission Tomography (PET)**

PET imaging is a widely used nuclear medicine technique which has the potential to study pathological events at a molecular level before extensive anatomical changes are observed on plain radiography. PET imaging is commonly combined with CT scanning in patients to provide both functional and anatomical information. [18F]-FDG accumulates in metabolically active cells after phosphorylation, and so is taken up by metabolically active macrophages within the TB granuloma and other inflammatory foci. A primary limitation of PET imaging for TB is the high cost and low availability in developing world. Increased PET uptake is well described in both pulmonary and extrapulmonary TB lesions (Matsuura et al., 2000; Bakheet et al., 1998; C.M. Yang et al., 2003), and can cause diagnostic uncertainty with malignancy and other infections (Chen et al., 2004; Li et al., 2008).

PET scanning is clinically useful in certain patients with TB. For example, when patients have normal radiology but symptoms highly suggestive of active TB, PET scans may identify occult foci of infection which can then be sampled to confirm the diagnosis and for culture (Figure 7, arrow). Furthermore, PET imaging has been proposed for monitoring the resolution of TB disease (Hofmeyr et al., 2007), although the benefit must be weighed against the increased radiation exposure. Current research questions which need to be

Molecular Imaging in TB: From the Bench to the Clinic 325

al., 1992; Kao et al., 1994; A. Yang et al, 2007), which significantly decreases the value of this radiotracer in the differential diagnosis of pulmonary TB from other lung pathologies.

A fundamental challenge of TB research is to develop applications which are useful in resource poor settings and can be deployed with minimal investment in infrastructure, maintenance and staff expertise. Furthermore, any application needs to be equally applicable in urban and rural settings. Most modern imaging techniques are useful in the developed world, but are not available to the vast majority of patients with TB who live in

However, the detailed study of a small number of patients may identify pathophysiological markers of TB which can then be simplified to develop new diagnostic and therapeutic approaches applicable in resource poor settings. Ironically, when one considers molecular imaging of TB in this light, developing an imaging technique based on plain chest radiography is currently the only widely deliverable approach in the near future. For example, if a highly radio-dense specific TB ligand was developed, a diagnostic test might involve taking an initial chest x-ray, injecting the labelled ligand, and then taking a second x-ray to identify high uptake in the region of TB. This might be useful in the common clinical scenario when a patient presents with upper zone fibrosis, which may be caused by either old self-healed TB or new active TB. If the patient is sputum smear negative, an expensive and invasive bronchoalveolar lavage is required, so a non-invasive test to

Another frequent clinical scenario is a patient with immunological evidence of infection, but with a normal chest x-ray and a cryptic location of disease. An investigation whereby one could locate the site of disease for aspiration and culture analysis would be clinically useful. This assay might either rely on antimycobacterial ligands, potentially using the "dock and lock" strategy (Goldenberg et al., 2007), whereby a primary antibody is first injected which docks on the mycobacterial target, and then 24 hours later a second radiolabelled antibody is injected which locks onto the primary antibody, or alternatively might focus on the host immune response, such as looking for increased metalloproteinase activity at the site of

In additional to diagnosis, a secondary role of imaging is to determine the prognostic and therapeutic correlates of host immunity. Currently, standard treatment lasts for six months. A recent trial comparing short-course therapy for four months in patients with low risk features was stopped because of increased recurrence in the short course treatment group (Daley, 2010). We need better markers to identify patients who will respond rapidly to treatment and imaging modalities to define mycobacterial load, the effectiveness of the host

It is clear that molecular imaging technologies will play an important role in improving our understanding of the host-pathogen interactions that occur in animal models of TB, and should speed up preclinical testing of novel vaccine candidates and therapeutic regimes. In

**5. Future prospects**

resource-poor settings.

determine disease activity would be useful.

disease (Elkington et al., 2011).

immune response and TB cure.

**6. Conclusions**

addressed are whether PET imaging can be useful to define cure, especially in the context of drug-resistant TB where treatment regimes may exceed 18 months, and also investigate whether active foci can be identified in patients with clinically "latent" disease.

Fig. 7. PET imaging reveals increased uptake of [18F]-FDG in a right hilar lymph node, appearing as bright white (as indicated by arrow).

In addition to [18F]-FDG, a wide array of radiopharmaceuticals have been developed at the preclinical level which might be applied to TB (Signore & Glaudemans, 2011). However, the potential of these in man have not yet been confirmed. For example, radiolabelling the antibiotic ciprofloxacin looked promising initially to investigate cryptic foci of infection (Britton et al., 2002), but has not entered clinical practice widely. The ability to detect a wide range of pathophysiological markers suggests that PET imaging may emerge as a powerful modality to investigate the biology of TB in man, but currently most prospective candidates require further study in model systems before clinical studies in man can be considered.

#### **4.5 Single Photon Emission Computed Tomography (SPECT)**

A number of SPECT radiotracers have been applied to the management of TB including 99mTc-methoxyisobutylisonitrile (99mTc-MIBI) (Ahmadihosseini, 2008), 67Ga (Liu et al., 2007) and 111In-octreotide (Vanhagen et al., 1994). 99mTc-MIBI is a widely used myocardial perfusion agent, which can accumulate in tumours and inflammatory lesions (Aktolun et al., 1991; Caner et al., 1992; Kao et al., 1994; A. Yang et al, 2007). Ahmadihosseini and colleagues studied 36 patients with either proven active or inactive treated pulmonary TB and found that 99mTc-MIBI uptake was increased in 23 out of 24 patients (95.8%) with active pulmonary TB but none of those with inactive TB (Ahmadihosseini et al., 2008). *M. tuberculosis* has been demonstrated to have significantly higher 99mTc-MIBI uptake compared with fibroblasts and myocytes cultures (Stefanescu et al., 2007), suggesting the bacilli themselves contribute to the signal detected on 99mTc-MIBI SPECT images.

A number of studies have found positive SPECT images in sputum smear negative patients subsequently found to have a positive sputum culture for *M. tuberculosis* (Ahmadihosseini et al., 2008; Önsel et al., 1996; Stefanescu et al., 2006), suggesting that SPECT imaging may be very useful while awaiting culture results. However, as previously stated, many benign and malignant etiologies can also demonstrate 99mTc-MIBI uptake (Aktolun et al., 1991; Caner et al., 1992; Kao et al., 1994; A. Yang et al, 2007), which significantly decreases the value of this radiotracer in the differential diagnosis of pulmonary TB from other lung pathologies.

### **5. Future prospects**

324 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

addressed are whether PET imaging can be useful to define cure, especially in the context of drug-resistant TB where treatment regimes may exceed 18 months, and also investigate

whether active foci can be identified in patients with clinically "latent" disease.

Fig. 7. PET imaging reveals increased uptake of [18F]-FDG in a right hilar lymph node,

In addition to [18F]-FDG, a wide array of radiopharmaceuticals have been developed at the preclinical level which might be applied to TB (Signore & Glaudemans, 2011). However, the potential of these in man have not yet been confirmed. For example, radiolabelling the antibiotic ciprofloxacin looked promising initially to investigate cryptic foci of infection (Britton et al., 2002), but has not entered clinical practice widely. The ability to detect a wide range of pathophysiological markers suggests that PET imaging may emerge as a powerful modality to investigate the biology of TB in man, but currently most prospective candidates require further study in model systems before clinical

A number of SPECT radiotracers have been applied to the management of TB including 99mTc-methoxyisobutylisonitrile (99mTc-MIBI) (Ahmadihosseini, 2008), 67Ga (Liu et al., 2007) and 111In-octreotide (Vanhagen et al., 1994). 99mTc-MIBI is a widely used myocardial perfusion agent, which can accumulate in tumours and inflammatory lesions (Aktolun et al., 1991; Caner et al., 1992; Kao et al., 1994; A. Yang et al, 2007). Ahmadihosseini and colleagues studied 36 patients with either proven active or inactive treated pulmonary TB and found that 99mTc-MIBI uptake was increased in 23 out of 24 patients (95.8%) with active pulmonary TB but none of those with inactive TB (Ahmadihosseini et al., 2008). *M. tuberculosis* has been demonstrated to have significantly higher 99mTc-MIBI uptake compared with fibroblasts and myocytes cultures (Stefanescu et al., 2007), suggesting the bacilli themselves contribute to

A number of studies have found positive SPECT images in sputum smear negative patients subsequently found to have a positive sputum culture for *M. tuberculosis* (Ahmadihosseini et al., 2008; Önsel et al., 1996; Stefanescu et al., 2006), suggesting that SPECT imaging may be very useful while awaiting culture results. However, as previously stated, many benign and malignant etiologies can also demonstrate 99mTc-MIBI uptake (Aktolun et al., 1991; Caner et

appearing as bright white (as indicated by arrow).

the signal detected on 99mTc-MIBI SPECT images.

**4.5 Single Photon Emission Computed Tomography (SPECT)**

studies in man can be considered.

A fundamental challenge of TB research is to develop applications which are useful in resource poor settings and can be deployed with minimal investment in infrastructure, maintenance and staff expertise. Furthermore, any application needs to be equally applicable in urban and rural settings. Most modern imaging techniques are useful in the developed world, but are not available to the vast majority of patients with TB who live in resource-poor settings.

However, the detailed study of a small number of patients may identify pathophysiological markers of TB which can then be simplified to develop new diagnostic and therapeutic approaches applicable in resource poor settings. Ironically, when one considers molecular imaging of TB in this light, developing an imaging technique based on plain chest radiography is currently the only widely deliverable approach in the near future. For example, if a highly radio-dense specific TB ligand was developed, a diagnostic test might involve taking an initial chest x-ray, injecting the labelled ligand, and then taking a second x-ray to identify high uptake in the region of TB. This might be useful in the common clinical scenario when a patient presents with upper zone fibrosis, which may be caused by either old self-healed TB or new active TB. If the patient is sputum smear negative, an expensive and invasive bronchoalveolar lavage is required, so a non-invasive test to determine disease activity would be useful.

Another frequent clinical scenario is a patient with immunological evidence of infection, but with a normal chest x-ray and a cryptic location of disease. An investigation whereby one could locate the site of disease for aspiration and culture analysis would be clinically useful. This assay might either rely on antimycobacterial ligands, potentially using the "dock and lock" strategy (Goldenberg et al., 2007), whereby a primary antibody is first injected which docks on the mycobacterial target, and then 24 hours later a second radiolabelled antibody is injected which locks onto the primary antibody, or alternatively might focus on the host immune response, such as looking for increased metalloproteinase activity at the site of disease (Elkington et al., 2011).

In additional to diagnosis, a secondary role of imaging is to determine the prognostic and therapeutic correlates of host immunity. Currently, standard treatment lasts for six months. A recent trial comparing short-course therapy for four months in patients with low risk features was stopped because of increased recurrence in the short course treatment group (Daley, 2010). We need better markers to identify patients who will respond rapidly to treatment and imaging modalities to define mycobacterial load, the effectiveness of the host immune response and TB cure.

#### **6. Conclusions**

It is clear that molecular imaging technologies will play an important role in improving our understanding of the host-pathogen interactions that occur in animal models of TB, and should speed up preclinical testing of novel vaccine candidates and therapeutic regimes. In

Molecular Imaging in TB: From the Bench to the Clinic 327

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#### **7. Acknowledgements**

This work has been supported in part by a grant to the Imaging TB consortium from the Bill and Melinda Gates Foundation TB Drug Accelerator Program. PE is funded by the UK National Institute for Health Research (NIHR) and is grateful for support from the NIHR Biomedical Research Centre (BRC) scheme at Imperial College London. SW is funded by a Sir Charles Hercus Fellowship from the Health Research Council of New Zealand. The authors would like to thank Prof. Ian Orme (Colorado State University) and Dr. Sanjay Jain MD (Johns Hopkins) for the provision of figures.

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imaging as a training aid for administration of substances in laboratory rodents.

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clinical observations of 99mTc-MIBI uptake correlate with P-glycoprotein expression in lung cancer. *Nuclear Medicine Communications*, Vol. 28, No. 9,

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**16**

*India*

**Data Mining Techniques in the**

**Diagnosis of Tuberculosis**

 *Bangalore Institute of Technology,*

 *PES Institute of Technology,*

T. Asha1, S. Natarajan2 and K. N. B. Murthy3 *1Department of Information Science & Engineering,*

*2,3Department of Information Science and Engineering,*

**Data mining** is the knowledge discovery process which helps in extracting interesting patterns from large amount of data. With the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, medical and scientific discovery (J.Han & M.Kamber,2006).

Humans have been manually extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automated approaches. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining (DM) is the process of applying these methods to data with the intention of uncovering hidden patterns.

Generally KDD is an iterative and interactive process involving several steps. This KDD process was chosen (Figure 1) according to UNESCO definition because of its simplicity and

The first step is to understand the application domain and to formulate the problem. This step is clearly a prerequisite for extracting useful knowledge and for choosing appropriate data mining methods in the third step according to the application target and the nature of data.

The second step is to collect and pre-process the data. Today's real-world databases are susceptible to noisy, missing, and inconsistent data due to their typically huge size (often

**1. Introduction**

**1.1 Data mining process**

**1.1.1 Problem identification and definition**

**1.1.2 Obtaining and preprocessing data**

comprehensiveness.

## **Data Mining Techniques in the Diagnosis of Tuberculosis**

T. Asha1, S. Natarajan2 and K. N. B. Murthy3 *1Department of Information Science & Engineering, Bangalore Institute of Technology, 2,3Department of Information Science and Engineering, PES Institute of Technology, India*

#### **1. Introduction**

332 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

Zonios, G., Bykowski, J. & Kollias, N. (2001). Skin melanin, hemoglobin, and light scattering

pp. 1452-1457, ISSN 0022-202X

properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy. *Journal of Investigative Dermatology*, Vol. 117, No. 6, December 2001,

> **Data mining** is the knowledge discovery process which helps in extracting interesting patterns from large amount of data. With the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, medical and scientific discovery (J.Han & M.Kamber,2006).

> Humans have been manually extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automated approaches. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining (DM) is the process of applying these methods to data with the intention of uncovering hidden patterns.

#### **1.1 Data mining process**

Generally KDD is an iterative and interactive process involving several steps. This KDD process was chosen (Figure 1) according to UNESCO definition because of its simplicity and comprehensiveness.

#### **1.1.1 Problem identification and definition**

The first step is to understand the application domain and to formulate the problem. This step is clearly a prerequisite for extracting useful knowledge and for choosing appropriate data mining methods in the third step according to the application target and the nature of data.

#### **1.1.2 Obtaining and preprocessing data**

The second step is to collect and pre-process the data. Today's real-world databases are susceptible to noisy, missing, and inconsistent data due to their typically huge size (often

Data Mining Techniques in the Diagnosis of Tuberculosis 335

The final step is to put the discovered knowledge in practical use. Putting the results in practical use is certainly the ultimate goal of the knowledge discovery. The information achieved can be used later to explain current or historical phenomenon, predict the future,

Data Mining functionalities are specifically of two categories: descriptive data mining and predictive data mining. Descriptive methods find human-interpretable patterns that describe the data. Predictive methods perform inference on the current data in order to

 Classification - Arranges the data into predefined groups. For example an email program might attempt to classify an email as legitimate or spam. Common algorithms include Decision Tree Learning, Nearest neighbor, Naive Bayesian classification and

 Association rule learning - Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to

Clustering - Is like classification but the groups are not predefined, so the algorithm will

Data mining finds its applications in various fields. **Web mining -** is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web Usage Mining (WUM), Web Content Mining (WCM) and Web Structure Mining (WSM). It is called **Spatial Data mining** if we apply data mining techniques to spatial data. **Multimedia Data mining**- is the application of data mining techniques to multimedia data such as audio, video, image, graphics etc. **Text mining**- applying data mining techniques on unstructured or semistructured text data such as news group, email, documents. Bioinformatics and Bio-data

Data mining draws ideas from many fields such as Machine learning/Artificial Intelligence, Pattern Recognition, Statistics, and Database Systems. In recent years, data mining has been widely used in the area of genetics, medicine, bioinformatics with its applications applied to biomedical data as facilitated by domain ontologies and mining clinical trial data which is

Different types of medical data are now available on the web, where DM algorithms and applications can be applied, helping in easy diagnosis. Efficient and scalable algorithms can

Regression -Attempts to find a function which models the data with the least error.

and help decision-makers make policy from the existed facts (ho, nd).

**1.1.5 Using discovered knowledge**

**1.2 Data mining tasks and functionalities**

make predictions (J.Han & M.Kamber, 2006).

The predictive tasks of data mining are:

The descriptive tasks of data mining are:

as "market basket analysis".

analysis on biological data.

also called medical data mining.

try to group similar items together.

Neural Network.

several gigabytes or more), and their likely origin from multiple, heterogeneous sources. Low quality data will lead to low quality mining results. Data pre-processing is an essential step for knowledge and data mining. Data pre-processing include the data integration, removal of noise or outliers, the treatment of missing data, data transformation and reduction of data etc. This step usually takes the most time needed for the whole KDD process.

Fig. 1. Structure of Data Mining (KDD) Process

#### **1.1.3 Selection of data mining / knowledge discovery in database**

The third step is data mining that extracts patterns and models hidden in data. This is an essential process where intelligent methods are applied in order to extract data patterns. In this step we have to first select data mining tasks and then data mining method. The major classes of data mining methods are predictive modeling such as classification and regression; segmentation (clustering) and association rules which are explained in detail in the next section.

#### **1.1.4 Interpretation and evaluation of results**

The fourth step is to interpret (post-process) discovered knowledge, especially the interpretation in terms of description and prediction which is the two primary goals of discovery system in practice. Experiments show that discovered patterns or models from data are not always of interest or direct use, and the KDD process is necessarily iterative with judgement of discovered knowledge. One standard way to evaluate induced rules is to divide the data into two sets, training on the first set and testing on the second. One can repeat this process a number of times with different splits, and then average the results to estimate the rules performance.

#### **1.1.5 Using discovered knowledge**

334 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

several gigabytes or more), and their likely origin from multiple, heterogeneous sources. Low quality data will lead to low quality mining results. Data pre-processing is an essential step for knowledge and data mining. Data pre-processing include the data integration, removal of noise or outliers, the treatment of missing data, data transformation and reduction of data etc. This step usually takes the most time needed for the whole KDD

process.

Fig. 1. Structure of Data Mining (KDD) Process

**1.1.4 Interpretation and evaluation of results**

estimate the rules performance.

the next section.

**1.1.3 Selection of data mining / knowledge discovery in database**

The third step is data mining that extracts patterns and models hidden in data. This is an essential process where intelligent methods are applied in order to extract data patterns. In this step we have to first select data mining tasks and then data mining method. The major classes of data mining methods are predictive modeling such as classification and regression; segmentation (clustering) and association rules which are explained in detail in

The fourth step is to interpret (post-process) discovered knowledge, especially the interpretation in terms of description and prediction which is the two primary goals of discovery system in practice. Experiments show that discovered patterns or models from data are not always of interest or direct use, and the KDD process is necessarily iterative with judgement of discovered knowledge. One standard way to evaluate induced rules is to divide the data into two sets, training on the first set and testing on the second. One can repeat this process a number of times with different splits, and then average the results to The final step is to put the discovered knowledge in practical use. Putting the results in practical use is certainly the ultimate goal of the knowledge discovery. The information achieved can be used later to explain current or historical phenomenon, predict the future, and help decision-makers make policy from the existed facts (ho, nd).

#### **1.2 Data mining tasks and functionalities**

Data Mining functionalities are specifically of two categories: descriptive data mining and predictive data mining. Descriptive methods find human-interpretable patterns that describe the data. Predictive methods perform inference on the current data in order to make predictions (J.Han & M.Kamber, 2006).

The predictive tasks of data mining are:

The descriptive tasks of data mining are:

Data mining finds its applications in various fields. **Web mining -** is the application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web Usage Mining (WUM), Web Content Mining (WCM) and Web Structure Mining (WSM). It is called **Spatial Data mining** if we apply data mining techniques to spatial data. **Multimedia Data mining**- is the application of data mining techniques to multimedia data such as audio, video, image, graphics etc. **Text mining**- applying data mining techniques on unstructured or semistructured text data such as news group, email, documents. Bioinformatics and Bio-data analysis on biological data.

Data mining draws ideas from many fields such as Machine learning/Artificial Intelligence, Pattern Recognition, Statistics, and Database Systems. In recent years, data mining has been widely used in the area of genetics, medicine, bioinformatics with its applications applied to biomedical data as facilitated by domain ontologies and mining clinical trial data which is also called medical data mining.

Different types of medical data are now available on the web, where DM algorithms and applications can be applied, helping in easy diagnosis. Efficient and scalable algorithms can

Data Mining Techniques in the Diagnosis of Tuberculosis 337

**Tuberculosis (TB)** is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly *Mycobacterium tuberculosis*. It usually spreads through the air and attacks low immune bodies such as patients with Human Immunodeficiency Virus (HIV). It is a disease which can affect virtually all organs, not sparing even the relatively inaccessible sites. The microorganisms usually enter the body by inhalation through the lungs. They spread from the initial location in the lungs to other parts of the body via the blood stream. They present a diagnostic dilemma even for physicians with a great deal of experience in this disease. Hence Tuberculosis (TB) is a contagious bacterial disease caused by mycobacterium which affects usually lungs and is

It is a great problem for most developing countries because of the low diagnosis and treatment opportunities. Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism. Thus, tuberculosis is a great health concern all

Symptoms of TB depend on where in the body the TB bacteria are growing. TB bacteria usually grow in the lungs. TB in the lungs may cause symptoms such as a bad cough that lasts 3 weeks or longer pain in the chest coughing up blood or sputum. Other symptoms of active TB disease are: weakness or fatigue, weight loss, no appetite, chills, fever and

Although common and deadly in the third world, Tuberculosis was almost non-existent in the developed world, but has been making a recent resurgence. Certain drug-resistant strains are emerging and people with immune suppression such as AIDS or poor health are

The medical dataset we are using includes 700 real records of patients suffering from TB obtained from a city hospital. The entire dataset is put in one file having many records. Each record corresponds to most relevant information of one patient. Initial queries by doctor as symptoms and some required test details of patients have been considered as main attributes. Totally there are 12 attributes (symptoms) and last attribute is considered as class in case of Associative Classification. The symptoms of each patient such as age, chronic cough(weeks), loss of weight, intermittent fever(days), night sweats, Sputum, Bloodcough, chestpain, HIV, radiographic findings, wheezing and TBtype are considered as attributes. Table 1 shows names of 12 attributes considered along with their Data Types (DT). Type N-

Association Rule Mining (ARM) is an important problem in the rapidly growing field called data mining and knowledge discovery in databases (KDD). The task of association rule mining is to mine a set of highly correlated attributes/features shared among a large number of records in a given database. For example, consider the sales database of a bookstore, where the

**2. Tuberculosis**

sweating at night.

becoming carriers.

**2.1 Data set description**

indicates numerical and C is categorical.

**3. Association Rule Mining**

often co-infected with HIV/AIDS.

over the world, and in India as well (wikipedia.org).

be implemented both in sequential and parallel mode thus improving the performance. Such type of mining is called medical data mining.

#### **1.3 Medical data mining**

In recent years, data mining has been widely used in the area of genetics and medicine, called medical data mining. In the past two decades we have witnessed revolutionary changes in biomedical research and bio-technology. There is an explosive growth of biomedical data, ranging from those collected in pharmaceutical studies and cancer therapy investigations to those identified in genomics and proteomics research. The rapid progress of biotechnology and bio-data analysis methods has led to the emergence and fast growth of a promising new field: Bioinformatics. On the other hand, recent progress in data mining research has led to the developments of numerous efficient and scalable methods for mining interesting patterns and knowledge in large databases, ranging from efficient classification methods to clustering, outlier analysis, frequent, sequential and structured pattern analysis methods, and visualization and spatial/temporal data analysis tools. The question becomes how to bridge the two fields, Data Mining and Bioinformatics, for successful data mining in biomedical data. Especially, we should analyze how data mining may help efficient and effective bio-medical data analysis and outline some research problems that may motivate the further developments of powerful data mining tools for bio data or medical data analysis.

Data mining is a process that involves aggregating raw data stored in a database and analyzing them to identify trends, patterns and anomalies. Medical data mining is an active research area under data mining since medical databases have accumulated large quantities of information about patients and their clinical conditions. Relationships and patterns hidden in this data can provide new medical knowledge as has been proved in a number of medical data mining applications. A Doctor quickly swung into action after a renowned pharmaceutical company in the USA announced in 2001 that it was withdrawing a cholesterol-lowering drug following the deaths of more than 30 people. Using his medical records database, his staff was able to identify all patients taking the cholesterol-lowering drug and notify them within 24 hours of the announcement. What the doctor did is technically known as Data Mining. Very few doctors, however, were able to act on the situation, because they did not have accessible raw data in the electronic format.

Not only does disciplined storage of medical data helps the physicians and healthcare institutions, but it also helps pharmaceutical companies to mine the data to see the trends in diseases. It also helps prioritize product development and clinical trials based on the accurate demands visible from the data that is mined.

Various data mining tasks can be applied on different diseases data set. This helps even the doctor to identify hidden associations between various symptoms. Research has been carried out on gene data, proteonomic data and attributes related to diseases covering even risk factors. Prediction of diseases has also been done on scanned images leading to medical imaging, which is the fastest growing area. Lot of Research has been carried out leading to breast cancer, liver diseases and other types of cancer and also diseases related to heart. There are very few articles related to Tuberculosis.

#### **2. Tuberculosis**

336 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

be implemented both in sequential and parallel mode thus improving the performance.

In recent years, data mining has been widely used in the area of genetics and medicine, called medical data mining. In the past two decades we have witnessed revolutionary changes in biomedical research and bio-technology. There is an explosive growth of biomedical data, ranging from those collected in pharmaceutical studies and cancer therapy investigations to those identified in genomics and proteomics research. The rapid progress of biotechnology and bio-data analysis methods has led to the emergence and fast growth of a promising new field: Bioinformatics. On the other hand, recent progress in data mining research has led to the developments of numerous efficient and scalable methods for mining interesting patterns and knowledge in large databases, ranging from efficient classification methods to clustering, outlier analysis, frequent, sequential and structured pattern analysis methods, and visualization and spatial/temporal data analysis tools. The question becomes how to bridge the two fields, Data Mining and Bioinformatics, for successful data mining in biomedical data. Especially, we should analyze how data mining may help efficient and effective bio-medical data analysis and outline some research problems that may motivate the further developments of powerful data mining tools for bio data or medical data

Data mining is a process that involves aggregating raw data stored in a database and analyzing them to identify trends, patterns and anomalies. Medical data mining is an active research area under data mining since medical databases have accumulated large quantities of information about patients and their clinical conditions. Relationships and patterns hidden in this data can provide new medical knowledge as has been proved in a number of medical data mining applications. A Doctor quickly swung into action after a renowned pharmaceutical company in the USA announced in 2001 that it was withdrawing a cholesterol-lowering drug following the deaths of more than 30 people. Using his medical records database, his staff was able to identify all patients taking the cholesterol-lowering drug and notify them within 24 hours of the announcement. What the doctor did is technically known as Data Mining. Very few doctors, however, were able to act on the

situation, because they did not have accessible raw data in the electronic format.

accurate demands visible from the data that is mined.

There are very few articles related to Tuberculosis.

Not only does disciplined storage of medical data helps the physicians and healthcare institutions, but it also helps pharmaceutical companies to mine the data to see the trends in diseases. It also helps prioritize product development and clinical trials based on the

Various data mining tasks can be applied on different diseases data set. This helps even the doctor to identify hidden associations between various symptoms. Research has been carried out on gene data, proteonomic data and attributes related to diseases covering even risk factors. Prediction of diseases has also been done on scanned images leading to medical imaging, which is the fastest growing area. Lot of Research has been carried out leading to breast cancer, liver diseases and other types of cancer and also diseases related to heart.

Such type of mining is called medical data mining.

**1.3 Medical data mining**

analysis.

**Tuberculosis (TB)** is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly *Mycobacterium tuberculosis*. It usually spreads through the air and attacks low immune bodies such as patients with Human Immunodeficiency Virus (HIV). It is a disease which can affect virtually all organs, not sparing even the relatively inaccessible sites. The microorganisms usually enter the body by inhalation through the lungs. They spread from the initial location in the lungs to other parts of the body via the blood stream. They present a diagnostic dilemma even for physicians with a great deal of experience in this disease. Hence Tuberculosis (TB) is a contagious bacterial disease caused by mycobacterium which affects usually lungs and is often co-infected with HIV/AIDS.

It is a great problem for most developing countries because of the low diagnosis and treatment opportunities. Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism. Thus, tuberculosis is a great health concern all over the world, and in India as well (wikipedia.org).

Symptoms of TB depend on where in the body the TB bacteria are growing. TB bacteria usually grow in the lungs. TB in the lungs may cause symptoms such as a bad cough that lasts 3 weeks or longer pain in the chest coughing up blood or sputum. Other symptoms of active TB disease are: weakness or fatigue, weight loss, no appetite, chills, fever and sweating at night.

Although common and deadly in the third world, Tuberculosis was almost non-existent in the developed world, but has been making a recent resurgence. Certain drug-resistant strains are emerging and people with immune suppression such as AIDS or poor health are becoming carriers.

#### **2.1 Data set description**

The medical dataset we are using includes 700 real records of patients suffering from TB obtained from a city hospital. The entire dataset is put in one file having many records. Each record corresponds to most relevant information of one patient. Initial queries by doctor as symptoms and some required test details of patients have been considered as main attributes. Totally there are 12 attributes (symptoms) and last attribute is considered as class in case of Associative Classification. The symptoms of each patient such as age, chronic cough(weeks), loss of weight, intermittent fever(days), night sweats, Sputum, Bloodcough, chestpain, HIV, radiographic findings, wheezing and TBtype are considered as attributes.

Table 1 shows names of 12 attributes considered along with their Data Types (DT). Type Nindicates numerical and C is categorical.

#### **3. Association Rule Mining**

Association Rule Mining (ARM) is an important problem in the rapidly growing field called data mining and knowledge discovery in databases (KDD). The task of association rule mining is to mine a set of highly correlated attributes/features shared among a large number of records in a given database. For example, consider the sales database of a bookstore, where the

Data Mining Techniques in the Diagnosis of Tuberculosis 339

Apriori algorithm employs two actions join step and prune step as explained in the

 Generate length (k+1) candidate itemsets from length k frequent itemsets [join step] Prune candidate itemsets containing subsets of length k that are infrequent [prune

Eliminate candidates that are infrequent, leaving only those that are frequent

Once the frequent itemsets from transactions in a database D have been found, it is straightforward to generate strong association rules from them where strong association rules satisfy both minimum support and minimum confidence. This is calculated from the

Confidence(A->B) = support\_count(AỤB) */* support\_count(A)

 For every nonempty subset s of l, output the rule " s -> (l-s)" if support\_count(l) */*  support\_count(s) is greater than or equal to min\_conf, where min\_conf is the

Since the processing of the Apriori algorithm requires plenty of time, its computational efficiency is a very important issue. In order to improve the efficiency of Apriori, many

researchers have proposed modified association rule-related algorithms.

following algorithm to find frequent itemsets.

*XY X Y sX sY* , :( ) ( ) ( )

minimum confidence threshold.

Reduce passes of transaction database scans

Facilitate support counting of candidates

Tedious workload of support counting for candidates

Multiple scans of transaction database

Huge number of candidates

**Improving Apriori: general ideas**

Shrink number of candidates

Count the support of each candidate by scanning the DB

Based on the above equation association rules can be generated as follows: For each frequent itemset l, generate all non empty subsets of l.

be frequent

**Apriori algorithm**

step]

**Rule Generation**

following equation

**Challenges of Apriori**

records represent customers and the attributes represent books. The mined patterns are the set of books most frequently bought together by the customer. An example could be that, 60% of the people who buy Design and Analysis of Algorithms also buy Data Structure. The store can use this knowledge for promotions, self-placement etc. There are many application areas for association rule mining techniques, which include catalog design, store layout, customer segmentation, telecommunication alarm diagnosis and so on.

Table 1. List of Attributes and their Datatypes

#### **3.1 Definition of association rule**

Here we give the classical definition of association rules. Let { t1, t2,…..tn} be a set of transactions and let I be a set of items, I={ I1,I2,….Im }. An association rule is an implication of the form XY, where X, Y are disjoint subsets of item I and X∩Y=ф. X is called the *antecedent* and Y is called the *consequent* of the rule. In general, a set of items such as the antecedent or consequent of a rule is called an *Itemset*. Each *itemset* has an associated measure of statistical significance called *support*. *support(x)=s* is the fraction of the transactions in the database containing X. The rule has a measure of strength called *confidence* defined as the ratio *support(X Ụ Y) / support(X)* (J.Han & M.Kamber, 2006).

Given a set of transactions T, the goal of association rule mining is to find all rules having support ≥ *minsup* threshold and confidence ≥ *minconf* threshold.

Mining Association rule is a Two-step approach:

	- Generate all itemsets whose support minsup.
	- Generate high confidence rules from each frequent itemset, where each rule is a binary partitioning of a frequent itemset.

#### **Frequent Itemset Generation**

The two important algorithms for frequent itemset generation are Apriori algorithm (first proposed by Agrawal, Imielinski and swami VLDB 1994) and Frequent pattern tree growth (FP-Tree) (FPgrowth—Han, Pei & Yin @SIGMOD'00).

Apriori algorithm employs two actions join step and prune step as explained in the following algorithm to find frequent itemsets.

	- Support of an itemset never exceeds the support of its subsets
		- *XY X Y sX sY* , :( ) ( ) ( )

#### **Apriori algorithm**

338 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

records represent customers and the attributes represent books. The mined patterns are the set of books most frequently bought together by the customer. An example could be that, 60% of the people who buy Design and Analysis of Algorithms also buy Data Structure. The store can use this knowledge for promotions, self-placement etc. There are many application areas for association rule mining techniques, which include catalog design, store layout, customer

> No **Name DT**  1 Age N 2 chroniccough(weeks) N 3 weightloss C 4 intermittentfever(days) N 5 nightsweats C 6 Bloodcough C 7 chestpain C 8 HIV C 9 Radiographicfindings C 10 Sputum C 11 wheezing C 12 TBType C

Here we give the classical definition of association rules. Let { t1, t2,…..tn} be a set of transactions and let I be a set of items, I={ I1,I2,….Im }. An association rule is an implication of the form XY, where X, Y are disjoint subsets of item I and X∩Y=ф. X is called the *antecedent* and Y is called the *consequent* of the rule. In general, a set of items such as the antecedent or consequent of a rule is called an *Itemset*. Each *itemset* has an associated measure of statistical significance called *support*. *support(x)=s* is the fraction of the transactions in the database containing X. The rule has a measure of strength called

Given a set of transactions T, the goal of association rule mining is to find all rules having

Generate high confidence rules from each frequent itemset, where each rule is a

The two important algorithms for frequent itemset generation are Apriori algorithm (first proposed by Agrawal, Imielinski and swami VLDB 1994) and Frequent pattern tree growth

*confidence* defined as the ratio *support(X Ụ Y) / support(X)* (J.Han & M.Kamber, 2006).

support ≥ *minsup* threshold and confidence ≥ *minconf* threshold.

Generate all itemsets whose support minsup.

binary partitioning of a frequent itemset.

(FP-Tree) (FPgrowth—Han, Pei & Yin @SIGMOD'00).

Mining Association rule is a Two-step approach:

**Frequent Itemset Generation**

segmentation, telecommunication alarm diagnosis and so on.

Table 1. List of Attributes and their Datatypes

**3.1 Definition of association rule**

	- Generate length (k+1) candidate itemsets from length k frequent itemsets [join step]
	- Prune candidate itemsets containing subsets of length k that are infrequent [prune step]
	- Count the support of each candidate by scanning the DB
	- Eliminate candidates that are infrequent, leaving only those that are frequent

#### **Rule Generation**

Once the frequent itemsets from transactions in a database D have been found, it is straightforward to generate strong association rules from them where strong association rules satisfy both minimum support and minimum confidence. This is calculated from the following equation

Confidence(A->B) = support\_count(AỤB) */* support\_count(A)

Based on the above equation association rules can be generated as follows:

#### **Challenges of Apriori**

#### **Improving Apriori: general ideas**

Since the processing of the Apriori algorithm requires plenty of time, its computational efficiency is a very important issue. In order to improve the efficiency of Apriori, many researchers have proposed modified association rule-related algorithms.

Data Mining Techniques in the Diagnosis of Tuberculosis 341

**4 <= Chronic cough (weeks) < 8**

**Null HIV = Positive HIV = Negative Sputum = Yes Sputum = Null**  7 8 9 10 11

In the above tables, note that Age is a numerical attribute and its cut off point is <25 & >=25. Similarly HIV is a categorical attribute where positive value is assigned one number and negative another. The value Null for categorical attribute weightloss is equivalent to No and is assigned a unique number. By using the schema table above we map each tuple in the original data of table 2 to a resulting normalized table shown in table 4. Resulting table has the same number of columns as the original table but filled with unique integer values.

Age **Chronic cough(weeks) Weight loss HIV Sputum**

a. **Frequent Itemsets from TB data:** The following figure 2 and 3 shows some of the frequent itemsets generated with 70% support, 90% confidence and 60% support,

[7] {chroniccough(weeks)<39.0 weightloss=null intermittentfever(days)<91.25} = 526

[17] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 chestpain=null} = 539

1 2 3 4 5 6

**Chronic cough (weeks) = Null**

**Weight loss = Yes**

**Age < 25**

**Age >= 25**

**Weight loss =**

Table 3. Schema Table

Table 4. Normalized Table

**3.2.2 TB rules generation**

[2] {weightloss=null} = 550

[8] {nightsweats=null} = 496 [9] {Bloodcough=null} = 671

[14] {chestpain=null} = 570

[1] {chroniccough(weeks)<39.0} = 692

[4] {intermittentfever(days)<91.25} = 669

**Chronic cough (weeks)< 4**

[3] {chroniccough(weeks)<39.0 weightloss=null} = 544

[6] {weightloss=null intermittentfever(days)<91.25} = 530

[10] {chroniccough(weeks)<39.0 Bloodcough=null} = 663

[13] {intermittentfever(days)<91.25 Bloodcough=null} = 645

[15 {chroniccough(weeks)<39.0 chestpain=null} = 564 [16] {intermittentfever(days)<91.25 chestpain=null} = 542

[11] {weightloss=null Bloodcough=null} = 534

[18] {Bloodcough=null chestpain=null} = 546

[5] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25} = 664

[12] {chroniccough(weeks)<39.0 weightloss=null Bloodcough=null} = 528

Fig. 2. Frequent Itemsets with 70%support and 90% confidence

#### **Advantages of frequent itemset generation and rule generation**

	- What products were often purchased together?— Beer and diapers?!
	- What are the subsequent purchases after buying a PC?
	- What kinds of DNA are sensitive to this new drug?
	- Can we automatically classify web documents?
	- Basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis.

#### **3.2 Tuberculosis association rules**

**T**uberculosis association rules can be generated by applying data mining ARM technique with the following steps:

#### **3.2.1 Pre-processing**

Incomplete, noisy, and inconsistent data are common among real world databases. Hence it is necessary to preprocess such data before using it. The most common topics under data preprocessing are Data cleaning, Data integration, Data Transformation, Data reduction, Data discretization and automatic generation of concept hierarchies.

Discretization and Normalization are the two data transformation procedures that help in representing the data and their relationships precisely in a tabular format that makes the database easy to understand and operationally efficient. This also reduces data redundancy and enhances performance.

The above TB attributes are normalized and discretized to a suitable binary format. A categorical data field has a value selected from an available list of values. Such data items can be normalized by allocating a unique column number to each possible value. Numerical data fields are discretized by taking values that are within some range defined by minimum and maximum limits. In such cases we can divide the given range into a number of subranges and allocate a unique column number to each sub-range respectively.

Here we give a small example of five patients medical records with five attributes. Table 2 shows original data. Table 3 contains schema of how the attributes are mapped to individual column numbers. Table 4 is the final translated or normalized data.

Table 2. Original (raw) Data

Table 3. Schema Table

340 Understanding Tuberculosis – Global Experiences and Innovative Approaches to the Diagnosis

Basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web

**T**uberculosis association rules can be generated by applying data mining ARM technique

Incomplete, noisy, and inconsistent data are common among real world databases. Hence it is necessary to preprocess such data before using it. The most common topics under data preprocessing are Data cleaning, Data integration, Data Transformation, Data reduction,

Discretization and Normalization are the two data transformation procedures that help in representing the data and their relationships precisely in a tabular format that makes the database easy to understand and operationally efficient. This also reduces data redundancy

The above TB attributes are normalized and discretized to a suitable binary format. A categorical data field has a value selected from an available list of values. Such data items can be normalized by allocating a unique column number to each possible value. Numerical data fields are discretized by taking values that are within some range defined by minimum and maximum limits. In such cases we can divide the given range into a number of sub-

Here we give a small example of five patients medical records with five attributes. Table 2 shows original data. Table 3 contains schema of how the attributes are mapped to individual

**Weight**

Yes Yes Null Yes Yes

**loss HIV Sputum**

Yes Yes Yes Null Yes

Negative Negative Negative Positive Positive

What products were often purchased together?— Beer and diapers?!

**Advantages of frequent itemset generation and rule generation**

 What are the subsequent purchases after buying a PC? What kinds of DNA are sensitive to this new drug? Can we automatically classify web documents?

log (click stream) analysis, and DNA sequence analysis.

 Pre-processing the dataset by discretizing and normalizing Generating rules by applying apriori on preprocessed range data

Data discretization and automatic generation of concept hierarchies.

ranges and allocate a unique column number to each sub-range respectively.

column numbers. Table 4 is the final translated or normalized data.

**cough(weeks)**

3 6 6 Null Null

**Age Chronic**

Table 2. Original (raw) Data

Finding inherent regularities in data

**3.2 Tuberculosis association rules**

with the following steps:

**3.2.1 Pre-processing**

and enhances performance.

Applications

In the above tables, note that Age is a numerical attribute and its cut off point is <25 & >=25. Similarly HIV is a categorical attribute where positive value is assigned one number and negative another. The value Null for categorical attribute weightloss is equivalent to No and is assigned a unique number. By using the schema table above we map each tuple in the original data of table 2 to a resulting normalized table shown in table 4. Resulting table has the same number of columns as the original table but filled with unique integer values.

Table 4. Normalized Table

#### **3.2.2 TB rules generation**

a. **Frequent Itemsets from TB data:** The following figure 2 and 3 shows some of the frequent itemsets generated with 70% support, 90% confidence and 60% support,

```
Fig. 2. Frequent Itemsets with 70%support and 90% confidence
 [1] {chroniccough(weeks)<39.0} = 692
 [2] {weightloss=null} = 550
 [3] {chroniccough(weeks)<39.0 weightloss=null} = 544
 [4] {intermittentfever(days)<91.25} = 669
 [5] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25} = 664
 [6] {weightloss=null intermittentfever(days)<91.25} = 530
 [7] {chroniccough(weeks)<39.0 weightloss=null intermittentfever(days)<91.25} = 526
 [8] {nightsweats=null} = 496
 [9] {Bloodcough=null} = 671
 [10] {chroniccough(weeks)<39.0 Bloodcough=null} = 663
 [11] {weightloss=null Bloodcough=null} = 534
 [12] {chroniccough(weeks)<39.0 weightloss=null Bloodcough=null} = 528
 [13] {intermittentfever(days)<91.25 Bloodcough=null} = 645
 [14] {chestpain=null} = 570
 [15 {chroniccough(weeks)<39.0 chestpain=null} = 564
 [16] {intermittentfever(days)<91.25 chestpain=null} = 542
 [17] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 chestpain=null} = 539
 [18] {Bloodcough=null chestpain=null} = 546
```
Data Mining Techniques in the Diagnosis of Tuberculosis 343

(1) {intermittentfever(days)<91.25 chestpain=null} -> {chroniccough(weeks)<39.0}

(7) {weightloss=null intermittentfever(days)<91.25} -> {Bloodcough=null} 97.54 (8) {chroniccough(weeks)<39.0 weightloss=null intermittentfever(days)<91.25} ->

(2) {intermittentfever(days)<91.25 Bloodcough=null chestpain=null} ->

(9) {chroniccough(weeks)<39.0 weightloss=null Bloodcough=null} ->

(11) {Bloodcough=null chestpain=null} -> {chroniccough(weeks)<39.0

(2) {chroniccough(weeks)<39.0 HIV=Negative} -> {TBtype=PTB} 100.0

(6) {HIV=Negative TBtype=PTB} -> {chroniccough(weeks)<39.0} 98.7 (7) {Bloodcough=null TBtype=PTB} -> {chroniccough(weeks)<39.0} 98.69 (8) {chroniccough(weeks)<39.0 nightsweats=null Bloodcough=null} ->

(12) {Bloodcough=null TBtype=PTB} -> {chroniccough(weeks)<39.0

(4) {Bloodcough=null HIV=Negative} -> {TBtype=PTB} 100.0

(3) {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 HIV=Negative} ->

(5) {intermittentfever(days)<91.25 Bloodcough=null HIV=Negative} -> {TBtype=PTB}

(9) {chroniccough(weeks)<39.0 TBtype=PTB} -> {intermittentfever(days)<91.25} 98.06 (10) {TBtype=PTB} -> {chroniccough(weeks)<39.0 intermittentfever(days)<91.25}

(11) {weightloss=null Bloodcough=null} -> {intermittentfever(days)<91.25} 96.81

(13) {chroniccough(weeks)<39.0 weightloss=null} -> {chestpain=null} 82.53

(12) {chestpain=null} -> {chroniccough(weeks)<39.0 Bloodcough=null} 94.73 (13) {chroniccough(weeks)<39.0 chestpain=null} -> {intermittentfever(days)<91.25

(14) {chestpain=null} -> {intermittentfever(days)<91.25 Bloodcough=null} 91.57 (15) {chestpain=null} -> {chroniccough(weeks)<39.0 intermittentfever(days)<91.25

(3) {nightsweats=null} -> {chroniccough(weeks)<39.0} 98.99 (4) {chestpain=null} -> {chroniccough(weeks)<39.0} 98.94 (5) {weightloss=null} -> {chroniccough(weeks)<39.0} 98.9 (6) {Bloodcough=null} -> {chroniccough(weeks)<39.0} 98.8

Fig. 4. Rule generation with 70%support and 90% confidence

(1) {HIV=Negative} -> {TBtype=PTB} 100.0

{intermittentfever(days)<91.25} 98.1

(10) {weightloss=null} -> {Bloodcough=null} 97.09

99.44

{chroniccough(weeks)<39.0} 99.42

{intermittentfever(days)<91.25} 97.15

intermittentfever(days)<91.25} 95.05

{Bloodcough=null} 97.52

Bloodcough=null} 92.02

Bloodcough=null} 91.05

{TBtype=PTB} 100.0

100.0

96.82

Fig. 5. Rule generation with 60% support and 80% confidence

intermittentfever(days)<91.25 HIV=Negative} 95.2

80%confidence. The Format of the rule is: [N] {I} = S, where N is a sequential number, I is the item set converted from normalized numerical value to schema text (symptoms) and S the support.

```
[1] {chroniccough(weeks)<39.0} = 692
[2] {weightloss=null} = 550
[3] {chroniccough(weeks)<39.0 weightloss=null} = 544
[4] {intermittentfever(days)<91.25} = 669
[5] {Bloodcough=null} = 671
[6] {chroniccough(weeks)<39.0 Bloodcough=null} = 663
[7] {weightloss=null Bloodcough=null} = 534
[8] {chestpain=null} = 570
[9] {chroniccough(weeks)<39.0 chestpain=null} = 564
[10] {weightloss=null chestpain=null} = 454
[11] {HIV=Negative} = 465
[12] {chroniccough(weeks)<39.0 HIV=Negative} = 459
[13] {intermittentfever(days)<91.25 HIV=Negative} = 455
[14] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 HIV=Negative} = 450
[15] {Bloodcough=null HIV=Negative} = 452
[16] {Sputum=yes} = 422
[17] {TBtype=PTB} = 472
[18] {chroniccough(weeks)<39.0 TBtype=PTB} = 466
[19] {intermittentfever(days)<91.25 TBtype=PTB} = 462
[20] {HIV=Negative TBtype=PTB} = 465
[21] {chroniccough(weeks)<39.0 HIV=Negative TBtype=PTB} = 459
[22] {intermittentfever(days)<91.25 HIV=Negative TBtype=PTB} = 455
[23] {intermittentfever(days)<91.25 Bloodcough=null HIV=Negative TBtype=PTB} = 442
[24] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 Bloodcough=null
HIV=Negative TBtype=PTB} = 437
```
Fig. 3. Frequent Itemsets with 60%support and 80% confidence

#### **b. Discovered TB Association rules**

Several medically important association rules are obtained after applying apriori algorithm to the normalized table. It takes the frequent itemsets in figure 2 and 3 and generates rules as shown in figure 4 and 5 respectively. Each association rule shows the relation between one symptom with the other. Data set was first tested by fixing support=70% and confidence=90%.We could get very few association rules, some are listed in figure 4. Rule 7 in figure 4 describes that if weightloss equals null and intermittent fever is less than 91 days implies Bloodcough is null with 97.5% confidence. Most of the rules show the relationship between only few attributes like weightloss, intermittent fever, Bloodcough and chest pain. All the attributes were not shown here. Next with 60% support and 80% confidence we could get large number of association rules, few listed in figure 5 that provides more relationship with many frequent attributes. Rule 1 in figure 5 says if HIV status is negative their TBtype is Pulmonary Tuberculosis (PTB) with 100% confidence. Rule 5 shows the relationship between intermittent fever, Bloodcough, HIV and PTB. Though all the rules

80%confidence. The Format of the rule is: [N] {I} = S, where N is a sequential number, I is the item set converted from normalized numerical value to schema text (symptoms) and S the

Fig. 3. Frequent Itemsets with 60%support and 80% confidence

[21] {chroniccough(weeks)<39.0 HIV=Negative TBtype=PTB} = 459 [22] {intermittentfever(days)<91.25 HIV=Negative TBtype=PTB} = 455

Several medically important association rules are obtained after applying apriori algorithm to the normalized table. It takes the frequent itemsets in figure 2 and 3 and generates rules as shown in figure 4 and 5 respectively. Each association rule shows the relation between one symptom with the other. Data set was first tested by fixing support=70% and confidence=90%.We could get very few association rules, some are listed in figure 4. Rule 7 in figure 4 describes that if weightloss equals null and intermittent fever is less than 91 days implies Bloodcough is null with 97.5% confidence. Most of the rules show the relationship between only few attributes like weightloss, intermittent fever, Bloodcough and chest pain. All the attributes were not shown here. Next with 60% support and 80% confidence we could get large number of association rules, few listed in figure 5 that provides more relationship with many frequent attributes. Rule 1 in figure 5 says if HIV status is negative their TBtype is Pulmonary Tuberculosis (PTB) with 100% confidence. Rule 5 shows the relationship between intermittent fever, Bloodcough, HIV and PTB. Though all the rules

[23] {intermittentfever(days)<91.25 Bloodcough=null HIV=Negative TBtype=PTB} = 442

[24] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 Bloodcough=null

[14] {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 HIV=Negative} = 450

**b. Discovered TB Association rules**

HIV=Negative TBtype=PTB} = 437

[1] {chroniccough(weeks)<39.0} = 692

[4] {intermittentfever(days)<91.25} = 669

[7] {weightloss=null Bloodcough=null} = 534

[10] {weightloss=null chestpain=null} = 454

[15] {Bloodcough=null HIV=Negative} = 452

[20] {HIV=Negative TBtype=PTB} = 465

[3] {chroniccough(weeks)<39.0 weightloss=null} = 544

[6] {chroniccough(weeks)<39.0 Bloodcough=null} = 663

[9] {chroniccough(weeks)<39.0 chestpain=null} = 564

[12] {chroniccough(weeks)<39.0 HIV=Negative} = 459 [13] {intermittentfever(days)<91.25 HIV=Negative} = 455

[18] {chroniccough(weeks)<39.0 TBtype=PTB} = 466 [19] {intermittentfever(days)<91.25 TBtype=PTB} = 462

[2] {weightloss=null} = 550

[5] {Bloodcough=null} = 671

[8] {chestpain=null} = 570

[11] {HIV=Negative} = 465

[16] {Sputum=yes} = 422 [17] {TBtype=PTB} = 472

support.

```
(1) {intermittentfever(days)<91.25 chestpain=null} -> {chroniccough(weeks)<39.0}
99.44
(2) {intermittentfever(days)<91.25 Bloodcough=null chestpain=null} ->
{chroniccough(weeks)<39.0} 99.42
(3) {nightsweats=null} -> {chroniccough(weeks)<39.0} 98.99
(4) {chestpain=null} -> {chroniccough(weeks)<39.0} 98.94
(5) {weightloss=null} -> {chroniccough(weeks)<39.0} 98.9
(6) {Bloodcough=null} -> {chroniccough(weeks)<39.0} 98.8
(7) {weightloss=null intermittentfever(days)<91.25} -> {Bloodcough=null} 97.54
(8) {chroniccough(weeks)<39.0 weightloss=null intermittentfever(days)<91.25} ->
{Bloodcough=null} 97.52
(9) {chroniccough(weeks)<39.0 weightloss=null Bloodcough=null} ->
{intermittentfever(days)<91.25} 97.15
(10) {weightloss=null} -> {Bloodcough=null} 97.09
(11) {Bloodcough=null chestpain=null} -> {chroniccough(weeks)<39.0
intermittentfever(days)<91.25} 95.05
(12) {chestpain=null} -> {chroniccough(weeks)<39.0 Bloodcough=null} 94.73
(13) {chroniccough(weeks)<39.0 chestpain=null} -> {intermittentfever(days)<91.25
Bloodcough=null} 92.02
(14) {chestpain=null} -> {intermittentfever(days)<91.25 Bloodcough=null} 91.57
(15) {chestpain=null} -> {chroniccough(weeks)<39.0 intermittentfever(days)<91.25
Bloodcough=null} 91.05
```
Fig. 4. Rule generation with 70%support and 90% confidence

(1) {HIV=Negative} -> {TBtype=PTB} 100.0 (2) {chroniccough(weeks)<39.0 HIV=Negative} -> {TBtype=PTB} 100.0 (3) {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 HIV=Negative} -> {TBtype=PTB} 100.0 (4) {Bloodcough=null HIV=Negative} -> {TBtype=PTB} 100.0 (5) {intermittentfever(days)<91.25 Bloodcough=null HIV=Negative} -> {TBtype=PTB} 100.0 (6) {HIV=Negative TBtype=PTB} -> {chroniccough(weeks)<39.0} 98.7 (7) {Bloodcough=null TBtype=PTB} -> {chroniccough(weeks)<39.0} 98.69 (8) {chroniccough(weeks)<39.0 nightsweats=null Bloodcough=null} -> {intermittentfever(days)<91.25} 98.1 (9) {chroniccough(weeks)<39.0 TBtype=PTB} -> {intermittentfever(days)<91.25} 98.06 (10) {TBtype=PTB} -> {chroniccough(weeks)<39.0 intermittentfever(days)<91.25} 96.82 (11) {weightloss=null Bloodcough=null} -> {intermittentfever(days)<91.25} 96.81 (12) {Bloodcough=null TBtype=PTB} -> {chroniccough(weeks)<39.0 intermittentfever(days)<91.25 HIV=Negative} 95.2 (13) {chroniccough(weeks)<39.0 weightloss=null} -> {chestpain=null} 82.53

Data Mining Techniques in the Diagnosis of Tuberculosis 345

appear as the class label. In order to perform AC, a classifier will first mine CARs from a given transaction and later select the most predictive rule to perform a classifier (Chien and Chen, 2010). AC generates CARs depending on the frequent item generation technique in mining rules. Despite its benefit, AC does propose challenges in its classification performance. The most important thing is to the approach in mining appropriate CARs for the classification and its pruning technology since AC will generate large number of frequent item sets due to its pruning algorithm. Its prominent pitfalls are in its incapability

Different approaches have been proposed for associative classification that has been found to outperform traditional classification algorithms. Some of AC algorithms include Classification based on Association (CBA), Classification based on Multiple Association Rules (CMAR), and Classification based on Predictive Association Rules (CPAR-Chien and Chen, 2010). Generally, AC consists of three main phases, which are rule generation, rule

of handling numerical data**.**

Fig. 6. Associative classification procedure

**4.1 Associative Classification Algorithms**

may not be interesting to users, only few rules like explained above gives very good description and some hidden relationship may also be found.

We could see from the following output that left side (Antecedent) and right side (consequent) of the rule keep on interchanging repeatedly, which can be pruned by applying some conditions on both antecedent and consequent of a rule.

The format is: (N) ANTECEDENT -> CONSEQUENT CONFIDENCE (%)

#### **4. Associative classification**

Association Rule Mining (ARM) as explained in section 3 is one of the most popular approaches in data mining and if used in the medical domain has a great potential to improve disease prediction. This results in large number of descriptive rules. Therefore ARM can be integrated within classification task to generate a single system called as Associative classification (AC) which is a better alternative for predictive analytics.

Classification based on association rules has been proved as very competitive (Liu.B et al., 1998). The general idea is to generate a set of association rules with a fixed consequent (involving the class attribute) and then use subsets of these rules to classify new examples. This approach has the advantage of searching a larger portion of the rule version space, since no search heuristics are employed, in contrast to Decision Tree and traditional classification rule induction. The extra search is done in a controlled manner enabled by the good computational behaviour of association rule discovery algorithms. Another advantage is that the produced rich rule set can be used in a variety of ways without relearning, which can be used to improve the classification accuracy ( Jorge and Azevedo, 2005).

The procedure of associative classification rule mining as shown in figure 6 is not much different from that of general association rule mining. A typical associative classification system is constructed in two stages: 1) discovering all the event association rules (in which the frequency of occurrences is significant according to some tests); 2) generating classification rules from the association patterns to build a classifier. In the first stage, the learning target is to discover the association rules inherent in a database, but generating frequent itemsets may prove to be quite expensive. The number of rules generated from association rule discovery is quite large. Hence rule pruning is required. Moreover, to avoid the problem of overfitting, proper rule pruning method is to be employed. Ranking of the rules is also important. When a test instance has more than one potentially applicable rules, rule ranking is necessary to prefer one rule over the others. In the second stage, the task is to select a set of relevant association rules discovered to construct a classifier given the predicting attribute.

For example given a rule X -> Y, AC will only consider rules having a target class as the consequent. This means the new integration focuses on a subset of association rules, whose right hand-sides are restricted to the classification class attribute. This type of rule is called Class Association Rules (CARs). While normal association rule allows more than one condition as its consequent and any item from *X* can be the consequent, CARs generated in AC limit the consequent to one fixed target class for each rule and item from *X* are forbid to