Title: Classification and prediction of Alzheimer's disease using multi-layer perceptron

Authors: Monika Jyotiyana; Nishtha Kesswani

Addresses: Central University of Rajasthan, Ajmer, Rajasthan, India ' Central University of Rajasthan, Ajmer, Rajasthan, India

Abstract: With the changing lifestyle, there is a tremendous increase in the cases of Alzheimer's disease. People are not able to pacify their urge of accurate diagnosis till date. The main reason for this increment is the changing lifestyle of today's generation because of which they are not able to meet their daily body requirements schedule which can keep them fit both physically and mentally. From childhood to adolescent to middle age, this carelessness does not show any signs of its glimpses but when a person hits the old age, it becomes prominent. In this paper, we have classified the patients suffering from Alzheimer's disease using the National Alzheimer's Coordinating Centre's (NACC's) database with the help of random forest (RF), support vector machine (SVM), K-nearest neighbour (KNN), linear discriminate analysis (LDA) and neural networks (NN). We also used the multi-layer perceptron (MLP) for classification of MRI data and the outcome signified that it proved to be the most competent approach with 94% accuracy.

Keywords: Alzheimer's disease; linear discriminate analysis; LDA; logistic regression; random forest; support vector machine; SVM; classification and prediction.

DOI: 10.1504/IJRIS.2020.111785

International Journal of Reasoning-based Intelligent Systems, 2020 Vol.12 No.4, pp.255 - 263

Received: 03 Sep 2019
Accepted: 04 Apr 2020

Published online: 14 Dec 2020 *

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