[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122392-en":3,"doc-seo-122392-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122392,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Explainable machine learning models for early Alzheimer’s disease detection using multimodal clinical data","Alzheimer’s disease poses a major global health challenge requiring early, accurate prediction to support timely intervention. While machine learning can improve AD risk detection, black-box limitations hinder clinical adoption because interpretability and transparency are insufficient. This study builds and evaluates explainable AI frameworks using multimodal patient data, enhancing model explanations with SHAP and LIME for both global and local insight, while preserving strong predictive performance.","Soladoye, Afeez Adekunle, Aderinto,  \nNicholas, Osho, Damilola and Olawade, David ORCID logoORCID: [https://orcid.org/0000-0003-0188-9836](https://orcid.org/0000-0003-0188-9836) (2025) Explainable machine learning models for early Alzheimer's disease detection using multimodal clinical data. International journal of medical informatics, 204. p. 106093.  \nDownloaded from: [https://ray.yorksj.ac.uk/id/eprint/12748/](https://ray.yorksj.ac.uk/id/eprint/12748/)  \nThe version presented here may differ from the published version or version of record. If you intend to cite from the work you are advised to consult the publisher's version: [https://doi.org/10.1016/j.ijmedinf.2025.106093](https://doi.org/10.1016/j.ijmedinf.2025.106093)  \nResearch at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form. Copyright of the items stored in RaY reside with the authors and/or other copyright owners. Users may access full text items free of charge, and may download a copy for private study or non-commercial research. For further reuse terms, see licence terms governing individual outputs. Institutional Repository Policy Statement  \nRaY  \nResearch at the University of York St John  \nFor more information please contact RaY at [ray@yorksj.ac.uk](ray@yorksj.ac.uk)  \nInternational Journal of Medical Informatics 204 (2025) 106093  \nContents lists available at ScienceDirect  \nInternational Journal of Medical Informatics  \njournal [homepage: www.elsevier.com/locate/ijmedinf](homepage: www.elsevier.com/locate/ijmedinf)  \n| Explainable machine learning models for early Alzheimer’s disease detection using multimodal clinical data |  |  |  |\n| --- | --- | --- | --- |\n| Afeez Adekunle Soladoyea, Nicholas Aderinto b, Damilola Oshoc, David B. Olawaded,e,f,* \u003Cbr>a Department of Computer Engineering, Federal University Oye-Ekiti, Ekiti, Nigeria\u003Cbr>b Department of Medicine, Ladoke Akintola University of Technology, Ogbomoso, Nigeria\u003Cbr>c Mental Health Unit, Essex Partnership University NHS Foundation Trust, Wickford, United Kingdom\u003Cbr>d Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdome Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom\u003Cbr>f Department of Public Health, York St John University, London, United Kingdom |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Alzheimer’s disease Explainable artificial intelligence Machine learning\u003Cbr>SHAP\u003Cbr>LIME |  | Background: Alzheimer’s disease (AD) represents a significant global health challenge requiring early and accurate prediction for effective intervention. While machine learning models demonstrate promising capabilities in AD prediction, their black-box nature limits clinical adoption due to a lack of interpretability and transparency.\u003Cbr>Objective: This study aims to develop and evaluate explainable artificial intelligence (XAI) frameworks for AD prediction using comprehensive multimodal patient data, with a focus on enhancing model interpretability through SHAP and LIME techniques.\u003Cbr>Methods: A comprehensive dataset of 2,149 patients aged 60–90 years was obtained from Kaggle, encompassing demographic, medical history, lifestyle, clinical measurements, cognitive assessments, and symptom data. Rigorous preprocessing included MinMax normalisation, Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, and Backward Elimination Feature Selection reduced 32 features to 26 optimal predictors. Six machine learning models were evaluated: K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, Stacked Ensemble, and Random Forest (RF). RF’s optimal hyperparameters were obtained using Ant colony Optimization Model interpretability was enhanced using SHAP and LIME frameworks for both global and loca","cbCaidTmwUcY581m","https://ap.wps.com/l/cbCaidTmwUcY581m","pdf",5717141,1,15,"English","en",105,"# Abstract\n# Introduction\n# Methods and Data\n## Models and Interpretability\n# Results\n# Conclusion and Future Work","[{\"question\":\"What problem does the study address in early Alzheimer’s disease detection?\",\"answer\":\"The study targets the need for early, accurate AD prediction and the barrier of ML black-box behavior that limits interpretability in clinical practice.\"},{\"question\":\"How does the research improve interpretability of machine learning models?\",\"answer\":\"It applies explainable AI techniques using SHAP and LIME to generate global and local explanations for the multimodal models.\"},{\"question\":\"What dataset and preprocessing steps are used?\",\"answer\":\"A Kaggle dataset of 2,149 patients aged 60–90 is used, with MinMax normalization, SMOTE for class imbalance, and backward elimination feature selection to reduce features before model training.\"}]","Explainable machine learning models for early Alzheimer’s disease detection using multimodal clinical data | 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