Method on Alzheimer’s Disease Classification Utilizing Fuzzy Logic Feature Selection and Heterogeneous Ensemble Learning

Abstract

Early diagnosis of dementia is critical for timely treatment and intervention. Alzheimer’s Disease(AD) classification is an effective method on identifying AD at its early stage. In this paper, a feature selection method using improved Gauss fuzzy logic is proposed. Firstly, the normalized feature importance scores are calculated utilizing mutual information and variance analysis respectively. Then the final feature importance score is obtained by using improved Gauss fuzzy logic. At last, the features for AD classification are selected in accordance with the feature importance score. Furthermore, the heterogeneous ensemble classifier is constructed to classify AD patient utilizing selected features, which using logistic regression, random forest, LightGBM, support vector machine and depth feedforward network as primary classifier and multinomial naive Bayes classifier as secondary classifier. The proposed AD classification method is evaluated on the TADPOLE dataset. The experimental results show that the proposed feature selection method is effective and the integrated classifier based on Bayesian fusion is better than other conventional classification model on AD classification using the proposed feature selection method.