[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120722-en":3,"doc-seo-120722-105":30,"detail-sidebar-cat-0-en-105":83},{"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},120722,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection","Although multiple brain lesions can contribute to dementia, their relationship to dementia, their interactions, and how to quantify them remain uncertain. This study applies machine learning feature selection to identify critical Alzheimer-related neuropathological features associated with dementia status. Using a cohort (n=186) from CFAS, it compares feature ranking and classification across neuropathologies. Seven feature-ranking methods consistently prioritized 22 of 34 features. The best model using the top eight features reached 79% sensitivity, 69% specificity, and 75% precision, yet 40.4% of cases were consistently misclassified. Results support machine learning to pinpoint key plaque, tangle, and cerebral amyloid angiopathy indices for dementia classification.","Rajab etal. Alzheimer’s Research & Therapy (2023) 15:47 [https://doi.org/10.1186/s13195-023-01195-9](https://doi.org/10.1186/s13195-023-01195-9)  \nAlzheimer’s Research & Therapy  \n RESEARCH Open Access  \nAssessment of Alzheimer-related pathologies of dementia using machine learning feature selection  \nMohammed D. Rajab 1,2, Emmanuel Jammeh1, Teruka Taketa1, Carol Brayne3, Fiona E. Matthews4, Li Su1,5, Paul G. Ince 1, Stephen B. Wharton 1, Dennis Wang1,2,6,7* and on behalf of the Cognitive Function and Ageing Neuropathology Study Group  \nAbstract  \nAlthough a variety of brain lesions may contribute to the pathological assessment of dementia, the relationship of these lesions to dementia, how they interact and how to quantify them remains uncertain. Systematically assessing neuropathological measures by their degree of association with dementia may lead to better diagnostic systems and treatment targets. This study aims to apply machine learning approaches to feature selection in order to identify critical features of Alzheimer-related pathologies associated with dementia. We applied machine learning techniques for feature ranking and classification to objectively compare neuropathological features and their relationship to dementia status during life using a cohort (n=186) from the Cognitive Function and Ageing Study (CFAS) . We first tested Alzheimer’s Disease and tau markers and then other neuropathologies associated with dementia. Seven feature ranking methods using different information criteria consistently ranked 22 out of the 34 neuropathology features for importance to dementia classification. Although highly correlated, Braak neurofibrillary tangle stage, beta-amyloid and cerebral amyloid angiopathy features were ranked the highest. The best-performing dementia classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity and 75% precision. However, when assessing all seven classifiers and the 22 ranked features, a substantial proportion (40 .4%) of dementia cases was consistently misclassified. These results highlight the benefits of using machine learning to identify critical indices of plaque, tangle and cerebral amyloid angiopathy burdens that may be useful for classifying dementia.  \nKeywords Dementia, Alzheimer’s, Feature selection, Machine learning, Neuropathology, Beta-amyloid  \n*Correspondence:  \nDennis Wang [dennis.wang@imperial.ac.uk](dennis.wang@imperial.ac.uk)  \n1 Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK  \n2 Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK  \n3 Cambridge Public Health, Cambridge CB2 1PZ, UK  \n4 Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE4 5PL, UK  \n5 Department of Psychiatry, University of Cambridge, Cambridge CB2 0SP, UK  \n6 Singapore Institute for Clinical Sciences, A*STAR, Singapore 117609, Singapore  \n7 National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (](http://creativecommons.org/lice","cbCaifn1NzT5C6Kg","https://ap.wps.com/l/cbCaifn1NzT5C6Kg","pdf",2013681,1,17,"English","en",105,"# Abstract\n# Introduction\n# Methods and Feature Selection Approach\n## Feature Ranking Methods\n## Classification Performance\n# Results and Key Findings\n# Discussion and Implications","[{\"question\":\"How accurate was the best classifier based on the selected top features?\",\"answer\":\"The best-performing classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity, and 75% precision, while 40.4% of dementia cases were consistently misclassified across models.\"}]","Assessment of Alzheimer-related pathologies of dementia using machine learning feature selection | PDF",1785731714,43,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"assessment-of-alzheimer-related-pathologies-of-dementia-using-machine-learning-feature-selection","",{"@graph":36,"@context":77},[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/assessment-of-alzheimer-related-pathologies-of-dementia-using-machine-learning-feature-selection/120722/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How accurate was the best classifier based on the selected top features?","Question",{"text":75,"@type":76},"The best-performing classifier using the top eight neuropathological features achieved 79% sensitivity, 69% specificity, and 75% precision, while 40.4% of dementia cases were consistently misclassified across models.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]