patients with liver cirrhosis ( lc ) induced by chronic hepatitis b ( chb ) are at high risk of developing hepatocellular carcinoma ( hcc ) [ 13 ] .
the proportion of people chronically infected with hepatitis b virus ( hbv ) is about 350 million people worldwide .
the lifetime risk of hbv carriers to develop cirrhosis is estimated to be more than 15% . at present ,
liver biopsy is still the golden standard for the evaluation of liver fibrosis and cirrhosis .
although histological assessment provides valuable information on the degree of necroinflammation and fibrosis in such patients , it is an invasive procedure associated with a finite albeit small risk of severe complications of 0.5% , patient discomfort , and expense .
moreover , a liver biopsy does not provide information regarding the balance between production and destruction of the extracellular matrix ( ecm ) or the rate of progression to cirrhosis .
over the past decade , attempts have been made to develop noninvasive methods to assess lc , including physical approaches and biological approaches .
transient elastography ( te ) , a recently developed noninvasive technique based on physical approach , has proved to have high diagnostic accuracy for lc [ 9 , 10 ] .
however , the accuracy of te is highly dependent on the experience of operators and clinicians , and its applicability is not as good as that of serum biomarkers with limitation in some patients , such as pacemakers , defibrillators , or overweight patients .
up to now , about 20 numerical scores or indices are reported mostly based on the routine laboratory parameters .
some models such as fibrotest and ast to platelet ratio index ( apri ) have been proposed for clinical application in patients with chronic hepatitis c ( chc ) [ 12 , 13 ] .
however , some models based on chc patients may not be suitable for predicting significant fibrosis and cirrhosis in hepatitis b - related fibrosis and can not reduce the number of liver biopsies .
recently , a validation study which examined 13 panels of indirect blood markers in chb patients , including fib-4 , apri , and forns , demonstrated that the performance for predicting liver fibrosis in chb patients had yet to be improved .
consequently , the objective of this work was to construct and evaluate a new classifier for predicting liver cirrhosis in chb patients using supervised machine learning methods based on the routine clinical parameters .
from october 2010 to march 2013 , a total of 239 subjects were collected , comprising 124 lc patients with chb and 115 patients with chb .
all patients were admitted to jinan military general hospital and changhai hospital of second military medical university .
this study was conducted in accordance with the declaration of helsinki and approved by the ethical committees of the hospitals mentioned above .
both hbv - induced chronic hepatitis and liver cirrhosis after hbv infection were diagnosed depending on the criteria established by the chinese medical association ( chinese society of hepatology and chinese society of infectious diseases ) .
all the participants were not coinfected with hiv or hepatitis c. patients were excluded if they consumed > 5  g of alcohol per day on average or were taking intravenous drugs or antihypertensive medications .
in addition , patients with evidence of a concurrent liver disorder such as primary biliary cirrhosis , autoimmune hepatitis , hcc , and wilson 's disease were excluded from this study .
an abdominal ultrasound scan was carried out on these patients to confirm normal hepatobiliary anatomy and to exclude biliary obstruction and hepatic space - occupying lesions .
clinical characteristics and laboratory parameters of patients were documented , including age , gender , total bilirubin ( tbil ) , creatinine ( cre ) , prothrombin time ( pt ) , albumin ( alb ) , platelet count ( plt ) , alanine transaminase ( alt ) , aspartate aminotransferase ( ast ) , alkaline phosphatase ( akp ) , red cell distribution width ( rdw ) , hemoglobin ( hgb ) , and mean corpuscular volume ( mcv ) .
for the patients with liver cirrhosis , the child - pugh score was calculated , as previously described . only the clinical characteristics and laboratory parameters of the first admission
were recorded for the patients receiving hospital care more than once between october 2010 and march 2013 .
similarly , the results of the first measurement were accepted regarding serial laboratory tests during hospitalization .
the aim of this study was to construct and evaluate the classifiers for prediction of liver cirrhosis with chronic hepatitis b based on the mentioned 13 routinely available clinical parameters .
the training set consisting of 120 patients ( 50.2% ) was used to build the classifier .
the remaining 119 patients ( 49.8% ) were used to construct the test set for validation .
waikato environment for knowledge analysis ( weka )   was employed for feature selection and classifier construction .
based on the training set , variable selection was performed by using the geneticsearch - based strategy in weka .
a list of clinical parameters sorted along the statistical difference between the two classes ( e.g. , lc and chb ) was obtained , which was used for classifier construction .
classifiers were constructed based upon the training set using multilayered perceptron ( mlp ) and nave bayes ( nb ) method in weka .
a 10-fold cross - validation was performed to avoid model - specific overfitting , as previously described .
briefly , all the entries were randomly divided into ten parts ; nine sets were used for training and the remaining one for testing .
the process was repeated ten times and the accuracy for true , false , and total accuracy calculated .
the final accuracy is the average of the accuracy in all ten tests . to evaluate the generalization performance of the two classifiers , in the next step
accuracy ( acc ) , sensitivity ( se ) , specificity ( sp ) , positive and negative predictive values , and likelihood ratios were calculated .   to further evaluate our classifier ,
the classifier with optimal cutoff was compared to two reported noninvasive indices using the test set : the apri and the fib-4 index .
the apri was calculated using ast [ u / l]/(uln of ast)/plt [ 10/l ]  100 .
the fib-4 index was calculated using ( age [ yr ]  ast [ u / l])/((plt [ 10/l ] )  ( alt [ u / l ] ) ) .  
student 's t - test or mann - whitney u test was used to test the difference between mean or median values . to compare the performance of a range of algorithms ,
star   was used to plot receiver operator curves ( roc ) and statistical comparison of area under curve ( auc ) of each roc .
all statistical analyses were performed using spss version 17.0 ( spss inc . , chicago , il , usa ) and graphpad prism software ( version 5.0 ) .
a total of 239 patients were enrolled in this study . clinical characteristics and laboratory findings from individuals with
there were significant differences between the two groups with respect to age , alp , albumin , tbil , pt , hb , mcv , rdw , plt , and cre .
the child - pugh scores of lc patients were as follows : a = 50 ( 40.3% ) , b = 45 ( 36.3% ) , and c = 29 ( 23.4% ) .   to construct and evaluate the classifier ,
the 239 patients were divided into training set ( n = 120 ) and test set ( n = 119 ) .
details of patients from the training set and test set in this study were given in table 2 .
after data preprocessing , feature variables were evaluated by using the geneticsearch - based strategy .
a panel of seven features was selected for classifier construction based upon the training set , including age , alt , ast , pt , plt , hgb , and rdw . to construct classifiers for predicting lc with chb ,
a preliminary test by tenfold cross - validation on the training set ( table 3 ) was carried out to evaluate the performance of the two classifiers .
mlp showed better results with acc of 82.2% and se of 80.6% but gave a poorer sp of 77.6% .
nb gave better results on specificity ( acc , 77.5% , se , 67.7% , and sp , 87.9% for nb ) .
the sensitivity of nb was not as high as expected . meanwhile , the auc of mlp was higher than that of nb ( p = 0.0125 ) . the ability of a classifier to discriminate data correctly in the test set is known as its generalization performance .
the mlp classifier ( table 4 ) gave better results on the test set , with se of 85.5% and sp of 89.5% ( overall accuracy 87.4% ) .
the auc of the mlp classifier was significantly better than that of nb ( p = 0.0133 ) .
the mlp classifier was then compared with two previously published noninvasive indices , including apri and fib-4 index .
the auc for predicting liver cirrhosis on the test set for all three algorithms is shown in figure 1 .
the aucs of the mlp classifier , apri , and fib-4 index were 0.942 , 0.817 , and 0.726 , respectively ( table 5 ) .
the auc of the mlp classifier was significantly better than those of fib-4 ( p = 0.0003 ) and apri ( p < 0.0001 ) .
furthermore , the prediction probability value of mlp increased significantly with the increase of child - pugh score in all liver cirrhosis patients ( figure 2 ) .
using star , the suggested cut - off values of all three algorithms were given in table 5 .
the accuracy , sensitivity , specificity , predictive values , and likelihood ratios of these algorithms were also calculated in the test set . with an optimal cut - off value of 0.281
, the mlp classifier showed the best acc ( 89.9% ) , se ( 95.2% ) , and sp ( 84.2% ) when compared to fib-4 and apri .
liver cirrhosis is the final stage of most of chronic liver diseases with the histological development of regenerative nodules surrounded by fibrous bands , which is mainly induced by hbv in china [ 3 , 21 , 22 ] . according to the latest european association for the study of the liver ( easl ) treatment guidelines on hbv
, liver biopsy should be performed with abnormal alt levels and high hbv dna levels ( > 2,000  iu / ml ) .
although liver biopsy has been the  golden standard  for evaluation of stage of liver fibrosis and cirrhosis , it is limited as it is an invasive procedure with significant expense , manpower issues , and some risks .
therefore , there is a need for a simple , reliable , and noninvasive alternative method for regular monitoring of disease progression . in this study
, we filtered out seven routine clinical parameters for the prediction of hbv - induced liver cirrhosis by statistical comparison of those of lc and chb , including age , alt , ast , pt , plt , hgb , and rdw .
we then investigated two supervised machine learning methods for predicting liver cirrhosis with these seven parameters .
we found that mlp gave better results ( auc = 0.942 ) between the two classifiers . when compared to the two reported noninvasive algorithms using routine clinical parameters ,
our results indicate that the mlp was superior to them in the independent test set .
the mlp classifier with optimal cut - off gives better accuracy ( 89.9% ) , higher sensitivity ( 95.2% ) , and acceptable sensitivity ( 84.2% ) for predicting hbv - induced liver cirrhosis .
a diagnostic model is considered as good if the auc is greater than 80% and excellent if the auc is greater than 90% .
therefore , we concluded that mlp was an excellent tool for hbv - induced liver cirrhosis prediction .
moreover , our finding confirmed that predicted probability value of mlp was increased with the raising of child - pugh scores in hbv - related liver cirrhosis patients . since
child - pugh score is a well - recognized prognostic index for liver diseases , our result suggests that mlp has potential prognostic value for liver disease .
first , mlp was based on seven routine clinical and laboratory parameters without any additional costs . therefore , mlp represents a cost - effective tool for hbv - induced liver cirrhosis prediction .
second , the parameters in mlp are easily acquired in clinical practice , even in community hospitals .
these advantages may facilitate the clinical utility of the mlp classifier for predicting liver cirrhosis in chb patients and reduce the number of liver biopsies .
apri and fib-4 are two widely used , noninvasive and inexpensive tools to predict liver cirrhosis .
it has satisfactory sensitivity and specificity together with a high predictive value for reducing the frequency with which biopsies need to be carried out to monitor the evolution of chc .
auc values of apri in chb fibrosis - related studies range between 0.541 and 0.86 [ 2633 ] . in a study of meta - analysis ,
the summary auc of apri was 0.75 with regard to hbv - related liver cirrhosis .
it was consistent with our result ( auc = 0.726 ) , which suggested that the diagnostic adequacy of apri was limited as a marker of liver fibrosis in chb patients compared to the patients with chc . in sterling 's report , fib-4 index was first created for predicting significant fibrosis in patients with hiv / hcv coinfection with an auc value of 0.737 . in our study , the auc of fib-4 was greater than apri in the test set which was consistent with the study of erdogan et al . .
in order to clarify whether mlp was better than apri and fib-4 for the prediction of lc with chb , we compared these tools in a head - to - head manner .
the results showed that the mlp classifier had higher auc when compared with apri or fib-4 .
therefore , we concluded that the mlp classifier had superior diagnostic efficiency than apri or fib-4 , two the most widely used noninvasive and inexpensive tools for liver cirrhosis prediction .
first used machine learning methods in predicting cirrhosis in patients with chc based on viral and clinical factors . in cazzaniga 's work , they obtained better auc for predicting cirrhosis in chc patients using artificial neural networks . recently , wang and his colleagues predicted significant liver fibrosis of chb patients using an artificial neural network based upon routine and serum markers . in their work ,
the aucs of training , validation , and test set were 0.883 , 0.884 , and 0.920 , respectively .
although 455 patients were enrolled in their study , there were 27.7% patients with significant liver fibrosis , which might generate predictive bias during modeling .
moreover , it requires further validation for predicting liver cirrhosis in chb patients because only 9 cirrhosis patients were included in their study .   in our study ,
seven common clinical parameters were selected to build the mlp classifier , including age , alt , ast , pt , plt , hgb , and rdw . among the seven routine clinical parameters , age , plt , ast ,
alt , pt , and hgb had been reported for predicting significant liver fibrosis [ 6 , 30 , 36 , 37 ] . in our published study , we found that rdw was increased with the worsening of hbv - related liver disease .
first , increased rdw was potentially associated with inflammation response during the process of liver fibrosis [ 40 , 41 ] .
second , the prevalence of renal failure is higher in patients with liver cirrhosis than in the general population , while increased rdw is related to impaired renal function . in our study , similar result was observed that the levels of serum creatinine and rdw were higher in liver cirrhosis patients .
firstly , only chinese chb patients were included in our study . due to small sample size
, verification of the mlp classifier with accuracy , sensitivity , and specificity in more large population from different race and regions would be important before considering clinical use .
secondly , our work was a retrospective study , and some clinical details of participants such as body mass index ( bmi ) , cholesterol levels , alpha - fetoprotein ( afp ) , inr , and duration of disease were not available in this study .
so it remains unclear whether including these clinical details in our mlp classifier will improve the diagnostic accuracy .
in addition , this deficiency also made us unable to compare our classifier with some reported algorithms , such as forns index , apga , and papas .
however , in validation studies reported recently [ 6 , 33 , 46 ] , fib-4 index gave the best diagnostic accuracy for the evaluation of hepatic fibrosis in patients with chb among these models , including forns index , apga , papas , and apri .
likewise , fib-4 index showed better auc than that of apri with optimal cutoff value in our work .
the main findings of this work are listed as follows . ( 1 ) the mlp classifier was developed for predicting liver cirrhosis in patients with chb using seven routine clinical parameters which are of low cost and easily implemented , even in community hospitals .
( 2 ) high auc of the mlp classifier which was superior to the two reported models could reduce the number of liver biopsies in clinical practice .
( 3 ) the mlp classifier has potential prognostic value for liver disease , especially in hbv - related liver cirrhosis patients . in conclusion
, we describe an mlp classifier , a noninvasive , accurate , inexpensive , and easily acquired tool for predicting and evaluating liver cirrhosis in chb patients .
other studies are necessary for further verification of its accuracy , sensitivity , and specificity in a larger population from different races and regions .
 
 background . liver cirrhosis ( lc ) is the final stage of most of chronic liver diseases and is almost caused by chronic hepatitis b ( chb ) in china . 
 liver biopsy is the reference method for the evaluation of liver cirrhosis . 
 however , it is an invasive procedure with inherent risk . 
 the aim of this study was to construct a new classifier based on the routine clinical markers for the prediction of hbv - induced lc . 
 subjects and methods . 
 we collected routine clinical parameters from 124 lc patients with chb and 115 with chb . 
 training set ( n = 120 ) and test set ( n = 119 ) were built for model construction and evaluation , respectively . 
 results . 
 we describe a new classifier , mlp , for prediction of lc with chb . 
 mlp was built with seven routinely available clinical parameters , including age , alt , ast , pt , plt , hgb , and rdw . with optimal cutoff 
 , we obtained a sensitivity of 95.2% , a specificity of 84.2% , and an overall accuracy of 89.9% on an independent test set , which were superior to those of fib-4 and apri . conclusions . 
 our study suggests that the mlp classifier can be implemented for discriminating lc and non - lc cohorts by using machine learning method based on the routine available clinical parameters . 
 it could be used for clinical practice in hbv - induced lc assessment . 
1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions