Title: Multiple-ensemble methods for prediction of Alzheimer disease

Authors: Divjot Singh; Ashutosh Mishra

Addresses: Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India ' Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

Abstract: Alzheimer's Disease (AD) is a neurodegenerative disease whose permanent cure is not yet available. However, its prediction at an early stage may increase the life span of a person by many years. The main predicament is to detect AD at an early stage and select the features responsible for it. The objective of this study was to predict AD at an early stage and identify the features that facilitate early prediction using ensemble learning. First, we implemented the ADNI data set on different machine-learning and deep-learning models. The proposed multiple ensemble method overcomes the limitations of existing models by applying feature selection for the early prediction, and it is observed that the best ensemble model is having the top 6-selected features and achieves an accuracy of 96.71% with higher ROC. Our model performed well compared with other machine and deep learning models.

Keywords: AD; Alzheimer disease; ML; machine learning; EL; ensemble learning; DL; deep learning; feature selection.

DOI: 10.1504/IJDMB.2021.124107

International Journal of Data Mining and Bioinformatics, 2021 Vol.26 No.1/2, pp.99 - 128

Received: 27 May 2021
Received in revised form: 09 May 2022
Accepted: 27 May 2022
Published online: 13 Jul 2022 *

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