{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:14:03Z","timestamp":1787019243982,"version":"build-2736575974"},"reference-count":15,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"Department of Defense","doi-asserted-by":"crossref","award":["W81XWH-12-2-0012"],"award-info":[{"award-number":["W81XWH-12-2-0012"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Am J Alzheimers Dis Other Demen"],"published-print":{"date-parts":[[2026,1,19]]},"abstract":"<jats:p>\n                    <jats:bold>Objective<\/jats:bold>\n                    : Alzheimer\u2019s disease (AD) continues to be a major challenge because handling high-dimensional data is time-consuming and expensive due to its complexity. A large feature space often increases computational costs and reduces model interpretability. This study addresses this problem by evaluating and comparing multiple feature selection techniques to identify the most informative biomarkers for AD diagnosis.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods<\/jats:bold>\n                    : Our study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to implement and test three feature selection approaches, visualization-based, filter-based, and wrapper-based, within a Naive Bayes (NB) classification framework.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results<\/jats:bold>\n                    : Based on the results of the analysis, the wrapper method achieved 96.77% classification accuracy, outperforming both visualization and filter methods with 86.19 and 91.87%, respectively. Interestingly, even when over 92.5% of the original features were removed the classifier still performed well, indicating that only a small set of features is necessary to ensure reliable diagnosis.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Discussion<\/jats:bold>\n                    : This study illustrates that strategically selecting features improves diagnostic accuracy while reducing computational burden, providing a more efficient framework for machine learning applications in Alzheimer's disease research.\n                  <\/jats:p>","DOI":"10.1177\/15333175261418472","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T02:53:46Z","timestamp":1768877626000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparison Study of Different Feature Selection Techniques for the Diagnosis of Alzheimer\u2019s Disease"],"prefix":"10.1177","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3955-8012","authenticated-orcid":false,"given":"Farah","family":"Masood","sequence":"first","affiliation":[{"name":"University of Baghdad"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0295-9691","authenticated-orcid":false,"given":"Abeer","family":"Al-Hyari","sequence":"additional","affiliation":[{"name":"Al-Balqa Applied University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wisam","family":"Al-Wajidi","sequence":"additional","affiliation":[{"name":"University of Baghdad"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"The Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207359"},{"key":"e_1_3_7_3_2","doi-asserted-by":"publisher","DOI":"10.32481\/djph.2021.09.005"},{"key":"e_1_3_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.06.013"},{"key":"e_1_3_7_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/IAICT.2019.8784779"},{"key":"e_1_3_7_6_2","first-page":"3078","article-title":"MR images classification of Alzheimer\u2019s disease based on deep belief network method","volume":"29","author":"Al-Majeed SA","year":"2020","unstructured":"Al-Majeed SA, Al-Tamimi MSH. MR images classification of Alzheimer\u2019s disease based on deep belief network method. Int J Adv Sci Technol. 2020;29:3078-3088. Available at. https:\/\/www.researchgate.net\/publication\/342183883_MR_Images_Classification_of_Alzheimer's_Disease_Based_on_Deep_Belief_Network_Method. Accessed [23 August 2025].","journal-title":"Int J Adv Sci Technol"},{"key":"e_1_3_7_7_2","doi-asserted-by":"publisher","DOI":"10.21123\/bsj.2024.9740"},{"key":"e_1_3_7_8_2","doi-asserted-by":"publisher","DOI":"10.7763\/IJMLC.2015.V5.517"},{"key":"e_1_3_7_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"e_1_3_7_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pscychresns.2008.07.016"},{"key":"e_1_3_7_11_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0077949"},{"key":"e_1_3_7_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.12.056"},{"key":"e_1_3_7_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22639"},{"key":"e_1_3_7_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_72"},{"key":"e_1_3_7_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116459"},{"key":"e_1_3_7_16_2","volume-title":"IDA Data Archive","author":"Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","unstructured":"Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI). IDA Data Archive. Available at. https:\/\/ida.loni.usc.edu\/services\/Menu\/IdaData.jsp. Published 2024. Accessed [23 August 2025]."}],"container-title":["American Journal of Alzheimer's Disease &amp; Other Dementias\u00ae"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15333175261418472","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15333175261418472","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15333175261418472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:20:50Z","timestamp":1777368050000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15333175261418472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":15,"alternative-id":["10.1177\/15333175261418472"],"URL":"https:\/\/doi.org\/10.1177\/15333175261418472","relation":{},"ISSN":["1533-3175","1938-2731"],"issn-type":[{"value":"1533-3175","type":"print"},{"value":"1938-2731","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]},"article-number":"15333175261418472"}}