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Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification
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 m...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571155/ https://www.ncbi.nlm.nih.gov/pubmed/36236757 http://dx.doi.org/10.3390/s22197661 |
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author | Chatterjee, Subhajit Byun, Yung-Cheol |
author_facet | Chatterjee, Subhajit Byun, Yung-Cheol |
author_sort | Chatterjee, Subhajit |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9571155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95711552022-10-17 Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification Chatterjee, Subhajit Byun, Yung-Cheol Sensors (Basel) Article 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. MDPI 2022-10-09 /pmc/articles/PMC9571155/ /pubmed/36236757 http://dx.doi.org/10.3390/s22197661 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chatterjee, Subhajit Byun, Yung-Cheol Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
title | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
title_full | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
title_fullStr | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
title_full_unstemmed | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
title_short | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
title_sort | voting ensemble approach for enhancing alzheimer’s disease classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571155/ https://www.ncbi.nlm.nih.gov/pubmed/36236757 http://dx.doi.org/10.3390/s22197661 |
work_keys_str_mv | AT chatterjeesubhajit votingensembleapproachforenhancingalzheimersdiseaseclassification AT byunyungcheol votingensembleapproachforenhancingalzheimersdiseaseclassification |