{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:17:56Z","timestamp":1782314276745,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea","award":["S3246057"],"award-info":[{"award-number":["S3246057"]}]},{"name":"Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea","award":["P0016977"],"award-info":[{"award-number":["P0016977"]}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","award":["S3246057"],"award-info":[{"award-number":["S3246057"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","award":["P0016977"],"award-info":[{"award-number":["P0016977"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s disease is dementia that impairs one\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22197661","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Voting Ensemble Approach for Enhancing Alzheimer\u2019s Disease Classification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1648-6095","authenticated-orcid":false,"given":"Subhajit","family":"Chatterjee","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jeju 63243, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jalz.2007.04.381","article-title":"Forecasting the global burden of Alzheimer\u2019s disease","volume":"3","author":"Brookmeyer","year":"2007","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"b931","DOI":"10.1136\/bmj.b931","article-title":"The national dementia strategy in England","volume":"338","author":"Burns","year":"2009","journal-title":"BMJ"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","article-title":"Automatic classification of patients with Alzheimer\u2019s disease from structural MRI: A comparison of ten methods using the ADNI database","volume":"56","author":"Cuingnet","year":"2011","journal-title":"NeuroImage"},{"key":"ref_4","unstructured":"Prince, M.J., Comas-Herrera, A., Knapp, M., Guerchet, M.M., and Karagiannidou, M. 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