A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method

Abstract

Based on proposed joint human connectome project multi-modal parcellation (JHCPMMP), the study on the binary classification of Alzheimer’s disease was conducted. We tried to build a novel classification model, which can be interpretative and have the ability to deal with the complexity and individual differences of brain networks. The subclass weighted logistic regression (SWLR) based on logistic regression was proposed in this paper. We conducted five groups of experiments, in which the accuracy of HC vs. AD was 95.8%, HC vs. EMCI was 91.6%, HC vs. LMCI was 93.7%, EMCI vs. LMCI was 89.5%, and LMCI vs. AD was 91.6%. In addition, we conducted a follow-up analysis of the coefficient matrix and found that the distribution of core deterioration brain regions in different stages is different in the development of Alzheimer’s disease. We located these brain regions in two-dimensional images and found that they generally show a trend of continuous counterclockwise migration.

Keywords

• Alzheimer’s disease
• human connectome project (HCP)
• multi-modal parcellation (MMP)
• mild cognitive impairment (MCI)
• subclass-weighting