[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125234-en":3,"doc-seo-125234-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125234,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Interpretable machine learning for precision cognitive aging","Interpretable machine learning for precision cognitive aging addresses the adoption gap in healthcare created by opaque complex models. It introduces and applies the Explainable Boosting Machine (EBM) to study how demographic, environmental, and lifestyle factors relate to cognitive performance in 3,482 healthy older adults. Compared with logistic regression, SVMs, random forests, multilayer perceptrons, and gradient boosting, EBM improves interpretability while keeping competitive predictive accuracy. Results reveal heterogeneous and activity-specific lifestyle effects across cognitive subgroups, supporting personalized strategies to mitigate cognitive decline.","TYPE Original Research PUBLISHED 16 May 2025  \nDOI 10.3389/fncom.2025.1560064  \nOPEN ACCESS  \nEDITED BY  \nMiodrag Zivkovic,  \nSingidunum University, Serbia  \nREVIEWED BY  \nNebojsa Bacanin, Singidunum University, Serbia Gloria A. Aguayo,  \nLuxembourg Institute of Health, Luxembourg  \n*CORRESPONDENCE  \nSylvain Moreno  \n [sylvain_moreno@sfu.ca](sylvain_moreno@sfu.ca)  \n†These authors have contributed equally to this work  \nRECEIVED 13 January 2025  \nACCEPTED 05 May 2025  \nPUBLISHED 16 May 2025  \nCITATION  \nMahamadou AJD, Rodrigues EA, Vakorin V, Antoine V and Moreno S (2025) Interpretable machine learning for precision cognitive aging.  \nFront. Comput. Neurosci. 19:1560064 .  \ndoi: 10.3389/fncom.2025.1560064  \nCOPYRIGHT  \n© 2025 Mahamadou, Rodrigues, Vakorin, Antoine and Moreno. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nInterpretable machine learning for precision cognitive aging  \nAbdoul Jalil Djiberou Mahamadou 1†, Emma A. Rodrigues 2†, Vasily Vakorin3,4, Violaine Antoine 5 and Sylvain Moreno 2,6*  \n1Stanford Center for Biomedical Ethics, Stanford University, Stanford, CA, United States, 2School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC, Canada, 3Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada, 4Royal Columbian Hospital, Fraser Health Authority, New Westminster, BC, Canada, 5CNRS ENSMSE LIMOS, Clermont Auvergne University, ClermontFerrand, France, 6Circle Innovation, Simon Fraser University, Surrey, BC, Canada  \nIntroduction: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a persistent gap exists between interpretability and performance, as black-box models, such as deep neural networks, often outperform white-box models, such as regression-based approaches. To bridge this gap, the Explainable Boosting Machine (EBM), a class of generalized additive models has been introduced, combining the strengths of interpretable and high-performing models. EBM may be particularly well-suited for cognitive health research, where traditional models struggle to capture nonlinear effects in cognitive aging and account for inter-and intra-individual variability.  \nMethods: This cross-sectional study applies EBM to investigate the relationship between demographic, environmental, and lifestyle factors, and cognitive performance in a sample of 3,482 healthy older adults. The EBM’s performance is compared against Logistic Regression, Support Vector Machines, Random Forests, Multilayer Perceptron, and Extreme Gradient Boosting, evaluating predictive accuracy and interpretability.  \nResults: The findings reveal that EBM provides valuable insights into cognitive aging, surpassing traditional models while maintaining competitive accuracy with more complex machine learning approaches. Notably, EBM highlights variations in how lifestyle activities impact cognitive performance, particularly differences between engaging in and refraining from specific activities, challenging regressionbased assumptions. Moreover, our results show that the effects of lifestyle factors are heterogeneous across cognitive groups, with some individuals demonstrating significant cognitive changes while others remain resilient to these influences.  \nDiscussion:Thesefindings highlight EBM’s potential incognitiveaging research,offering both interpretability and accuracy","cbCaicOKbnW0KRIc","https://ap.wps.com/l/cbCaicOKbnW0KRIc","pdf",748879,1,10,"English","en",105,"# Introduction\n## Explainable AI and the interpretability–performance gap\n# Methods\n## Dataset and EBM modeling approach\n## Comparison models and evaluation\n# Results\n## Predictive accuracy and interpretability findings\n## Lifestyle activity effects and heterogeneity\n# Discussion\n## Implications for precision cognitive aging and personalized strategies","[{\"question\":\"Why is explainable AI important for cognitive aging research?\",\"answer\":\"Many high-performing AI models are black boxes, limiting transparency, scientific interpretation, and accountability in healthcare and cognitive science. XAI improves trust and interpretability for decision-making.\"},{\"question\":\"How does the study use the Explainable Boosting Machine (EBM)?\",\"answer\":\"The study applies EBM to relate demographic, environmental, and lifestyle factors to cognitive performance in 3,482 healthy older adults. It evaluates both predictive accuracy and interpretability.\"},{\"question\":\"What do the results show about lifestyle factors and cognitive performance?\",\"answer\":\"EBM identifies variations in how lifestyle activities affect cognitive performance and shows that these effects are heterogeneous across cognitive groups. Some individuals exhibit significant cognitive changes while others remain resilient.\"}]","Interpretable machine learning for precision cognitive aging | PDF",1785897636,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"interpretable-machine-learning-for-precision-cognitive-aging","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/interpretable-machine-learning-for-precision-cognitive-aging/125234/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is explainable AI important for cognitive aging research?","Question",{"text":75,"@type":76},"Many high-performing AI models are black boxes, limiting transparency, scientific interpretation, and accountability in healthcare and cognitive science. 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