Classification of Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Controls with Multilayer Perceptron Neural Network and Neuropsychological Test Data

Ibrahim Almubark, Samah Alsegehy, Xiong Jiang, Lin-Ching Chang

Abstract

Recent advances in machine learning have shown outstanding performances in biological and medical data analysis to assist for early detection, diagnosis, and treatment of diseases. Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. In this study, multilayer perceptron (MLP) neural networks are developed to classify AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) subjects based upon the data from standard neuropsychological tests. Three neuropsychological tests from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog), Mini-Mental State Examination (MMSE), and Functional Activities Questionnaire (FAQ), were used to train MLP neural networks. We first build three MLP models that can classify AD vs. CN, AD vs. MCI, and MCI vs. CN. We then construct a 3-way MLP classifier to classify AD vs. MCI vs. CN. Finally, we propose a cascade 3-way classification method to further improve the model performance. Using the neuropsychological test data from ADNI database, our result shows the pairwise MLP models (i.e., AD vs. CN, AD vs. MCI, and MCI vs. CN) have the accuracy of 99.760.48, 89.643.94, and 90.812.91, respectively. The multi-class MLP model has an average accuracy of 84.283.66, and the proposed cascaded MLP approach further improves the performance of the multi-class classification with an average accuracy of 86.263.15.

Download


Paper Citation