Classification of Patients with Alzheimer’s Disease Based on Structural MRI Using Locally Linear Embedding (LLE)
Several methods have been used to classify patients with Alzheimer’s disease (AD) or its prodromal stage, mild cognitive impairment (MCI) from cognitive normal (CN) based on T1-weighted MRI. In this study, we used LLE to discriminate 453 subjects form the ADNI database. We conducted six pair wise classification experiments: CN (cognitive normal) vs. sMCI (MCI who kept stability and had not converted to AD within 18 months, stable MCI — sMCI), CN vs. cMCI (MCI who had converted to AD within 18 months, converters MCI — cMCI), CN vs. AD, sMCI vs. cMCI, sMCI vs. AD, and cMCI vs. AD. Each of them was repeated for 10 times. The proposed method got the average accuracy of 0.67, 0.79, 0.85, 0.72, 0.75 and 0.65, respectively. The outcomes suggested that the LLE method is useful in the clinical diagnosis and the prediction of AD.