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Article

Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification

by
Subhajit Chatterjee
1 and
Yung-Cheol Byun
2,*
1
Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
2
Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(19), 7661; https://doi.org/10.3390/s22197661
Submission received: 3 September 2022 / Revised: 1 October 2022 / Accepted: 5 October 2022 / Published: 9 October 2022
(This article belongs to the Collection Biomedical Imaging and Sensing)

Abstract

Alzheimer’s disease is dementia that impairs one’s thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer’s disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained.
Keywords: Alzheimer’s disease; deep learning; classification; ensemble learning; MRI data Alzheimer’s disease; deep learning; classification; ensemble learning; MRI data

Share and Cite

MDPI and ACS Style

Chatterjee, S.; Byun, Y.-C. Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification. Sensors 2022, 22, 7661. https://doi.org/10.3390/s22197661

AMA Style

Chatterjee S, Byun Y-C. Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification. Sensors. 2022; 22(19):7661. https://doi.org/10.3390/s22197661

Chicago/Turabian Style

Chatterjee, Subhajit, and Yung-Cheol Byun. 2022. "Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification" Sensors 22, no. 19: 7661. https://doi.org/10.3390/s22197661

APA Style

Chatterjee, S., & Byun, Y.-C. (2022). Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification. Sensors, 22(19), 7661. https://doi.org/10.3390/s22197661

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