Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

Author:

Tanveer M.1,Richhariya B.1,Khan R. U.1,Rashid A. H.2,Khanna P.3,Prasad M.4,Lin C. T.4

Affiliation:

1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India

2. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India

3. PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India

4. Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia

Abstract

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.

Funder

Council of Scientific 8 Industrial Research (CSIR), New Delhi, INDIA under Extra Mural Research (EMR) Scheme

Department of Science and Technology, INDIA as Ramanujan fellowship

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

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