{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T15:48:13Z","timestamp":1788018493435,"version":"build-2784847793"},"reference-count":26,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":38,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp;amp; Dem Diag Ass &amp;amp; Dis Mo"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>INTRODUCTION<\/jats:title><jats:p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia. Early diagnosis is vital. We developed an interpretable machine learning (ML) model for early AD prediction using open clinical data.<\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p>Data from 2149 adults (60\u201390\u00a0years) were obtained from Kaggle. After preprocessing and feature engineering, tree\u2010based models were trained. A stacking ensemble model combining Gradient Boosting and XGBoost was trained, with Logistic Regression as the meta\u2010learner. SHapley Additive exPlanations (SHAP) provided interpretability. Performance was measured by accuracy, precision, recall, F1 score, ROC and AUC.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The stacked ensemble achieved 97% accuracy (AUC\u00a00.97), with 0.97 precision, 0.94 recall, and 0.96 F1 score for AD. SHAP identified memory complaints, Mini\u2010Mental State Examination (MMSE), functional assessment, behavioral symptoms, cholesterol, and lifestyle factors (activity, diet, sleep) as top predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>The ensemble model, enhanced by SHAP analysis, provides accurate and interpretable AD risk predictions with potential applicability in future clinical decision support systems.<\/jats:p><\/jats:sec><jats:sec><jats:title>Highlights<\/jats:title><jats:p><jats:list list-type=\"bullet\">\n<jats:list-item><jats:p>Developed an ensemble machine learning (ML) model for early Alzheimer's disease (AD) prediction.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Achieved 97% accuracy using stacked XGBoost and Gradient Boosting.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>SHapley Additive exPlanations (SHAP) analysis identified key cognitive and lifestyle\u2010related risk factors.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Model interprets AD risk using explainable artificial intelligence (AI) for clinical applicability.<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Utilized open\u2010access dataset to ensure reproducibility and transparency.<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1002\/dad2.70162","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T17:00:24Z","timestamp":1754672424000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Optimizing Alzheimer's disease prediction through ensemble learning and feature interpretability with SHAP\u2010based feature analysis"],"prefix":"10.1002","volume":"17","author":[{"given":"Md Kamrul","family":"Hossain","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering Daffodil International University  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afrina","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Daffodil International University  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Mominul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Daffodil International University  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoriful Hassan","family":"Sourav","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Daffodil International University  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6443-3265","authenticated-orcid":false,"given":"Md. Monir Hossain","family":"Shimul","sequence":"additional","affiliation":[{"name":"Department of Public Health Daffodil International University  Dhaka Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules25245789"},{"key":"e_1_2_11_3_1","unstructured":"World Health Organization.Dementia fact sheet.2023. Accessed 11 Apr 2025https:\/\/www.who.int\/news\u2010room\/fact\u2010sheets\/detail\/dementia"},{"key":"e_1_2_11_4_1","unstructured":"Alzheimer's Disease International.Dementia statistics.2020. 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