[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122844-en":3,"doc-seo-122844-105":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},122844,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies","Early diagnosis of dementia is challenging due to the time and resources required for neuropsychological and pathological assessments. With increasing use of machine learning to assess neuropathology features, this work evaluates how feature selection affects classification and which features matter most. Neuropathology features are filtered and ranked objectively across two independent cohorts, CFAS and ADNI, using reliefF and least loss methods, then examined for bias from feature–feature correlations. Braak stage emerges as consistently top-ranked and minimally correlated with others. Using a small set of highly ranked features can match or improve dementia classification performance.","Received: 6 August 2023  \nAccepted: 30 January 2024  \nDOI: 10.1111/bpa.13247  \nRESEARCH ARTICLE  \nRanking and filtering of neuropathology features in the machine learning evaluation of dementia studies  \nMohammed D. Rajab 1,2 | Teruka Taketa 1 | Stephen B. Wharton 1 |  \nDennis Wang 1,2,3,4,5  | Cognitive Function and Ageing Neuropathology Study, and for the Alzheimer’s Disease Neuroimaging Initiative  \n1Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK  \n2Department of Computer Science, University of Sheffield, Sheffield, UK  \n3Singapore Institute Clinical Sciences, Agency for Science Technology and Research  \n(A*STAR), Singapore, Singapore 4Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Singapore  \n5National Heart and Lung Institute, Imperial College London, London, UK  \nCorrespondence  \nDennis Wang, National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK.  \nEmail: [dennis.wang@imperial.ac.uk](dennis.wang@imperial.ac.uk)  \nFunding information  \nAcademy of Medical Sciences, Grant/Award Number: APR7_ 1002; Engineering and Physical Sciences Research Council, Grant/Award Number: EP/V029045/1; Medical Research Council, Grant/Award Numbers: MRC/ G9901400, U.1052.00.0013, G0900582; National Institutes of Health, Grant/Award Number: U01 AG024904; Department of Defense, Grant/Award Number: W81XWH- 12-2-0012; Alzheimer’s Society, Grant/Award Numbers: AS-PG-14-015, AS-PG-17-007; NIHR Sheffield Biomedical Research Centre; Saudi Arabia Ministry of Education, PhDScholarship  \nAbstract  \nEarly diagnosis of dementia diseases, such as Alzheimer’s disease, is difficult because of the time and resources needed to perform neuropsychological and pathological assessments. Given the increasing use of machine learning methods to evaluate neuropathology features in the brains of dementia patients, it is important to investigate how the selection of features may be impacted and which features are most important for the classification of dementia. We objectively assessed neuropathology features using machine learning techniques for filtering features in two independent ageing cohorts, the Cognitive Function and Aging Studies (CFAS) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) . The reliefF and least loss methods were most consistent with their rankings between ADNI and CFAS; however, reliefF was most biassed by feature–feature correlations. Braak stage was consistently the highest ranked feature and its ranking was not correlated with other features, highlighting its unique importance. Using a smaller set of highly ranked features, rather than all features, can achieve a similar or better dementia classification performance in CFAS (60%–70% accuracy with Naïve Bayes) . This study showed that specific neuropathology features can be prioritised by feature filtering methods, but they are impacted by feature–feature correlations and their results can vary between cohort studies. By understanding these biases, we can reduce discrepancies in feature ranking and identify a minimal set of features needed for accurate classification of dementia.  \nKEYW ORD S  \nAlzheimer’s disease, collinearity, dementia, feature selection, machine learning, neuropathology  \n1 | INTRODUCTION  \nDementia poses a significant global challenge, affecting the lives of individuals, their families, and caregivers [1] . The economic burden of dementia was estimated to exceed $818 billion in 2015, and the number of people living with dementia is expected to surpass 75 million by  \n2030 [2] . Early diagnosis and intervention are crucial in mitigating the negative impact of dementia [3] . However, identifying the determinants of dementia can be difficult because of its complex spectrum of characteristics, encompassing various disorders with distinct pathologies. Alzheimer’s disease (AD) is the most common form of dementia, characterised by the presence of  \nThis is an open ac","cbCaifgr5ZCalZcn","https://ap.wps.com/l/cbCaifgr5ZCalZcn","pdf",2728525,1,16,"English","en",105,"# Abstract\n# Introduction\n## Dementia burden and need for early diagnosis\n## Feature selection in biomedical datasets\n## Prior work on filter methods for dementia and Alzheimer’s disease","[{\"question\":\"Why is early diagnosis of dementia difficult?\",\"answer\":\"Early diagnosis is difficult because neuropsychological and pathological assessments require substantial time and resources.\"},{\"question\":\"How did the study evaluate neuropathology features for dementia classification?\",\"answer\":\"It applied machine learning-based feature filtering and ranking methods across two independent ageing cohorts (CFAS and ADNI) and assessed which features contribute most to classification.\"},{\"question\":\"Which neuropathology feature was most consistently ranked and why is it important?\",\"answer\":\"Braak stage was consistently the highest ranked feature, with ranking not correlated with other features, highlighting its unique importance.\"}]","Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies | 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is early diagnosis of dementia difficult?","Question",{"text":75,"@type":76},"Early diagnosis is difficult because neuropsychological and pathological assessments require substantial time and resources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did the study evaluate neuropathology features for dementia classification?",{"text":80,"@type":76},"It applied machine learning-based feature filtering and ranking methods across two independent ageing cohorts (CFAS and ADNI) and assessed which features contribute most to classification.",{"name":82,"@type":73,"acceptedAnswer":83},"Which neuropathology feature was most consistently ranked and why is it important?",{"text":84,"@type":76},"Braak stage was consistently the highest ranked feature, with ranking not correlated with other features, highlighting its unique 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