Multiple-ensemble methods for prediction of Alzheimer disease
by Divjot Singh; Ashutosh Mishra
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 26, No. 1/2, 2021

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.

Online publication date: Wed, 13-Jul-2022

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