[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127454-en":3,"doc-seo-127454-105":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},127454,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Converse or reverse? Machine-learning modeling for disease progression - A study based on Alzheimer’s disease continuum cohort","Longitudinal trajectories from healthy aging to Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) reflect complex mechanisms and heterogeneous outcomes, including stable states, disease conversion, and reverse progression. A systems approach evaluated five machine-learning models—Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, and Convolutional Neural Networks—using the ADNI cohort across ADNI1, ADNIGO, ADNI2, and ADNI3 phases. Random Forest delivered the strongest performance (macro sensitivity 70.8%, specificity 96.8%). It highlighted visuospatial and memory-related dysfunction, amyloid-related neuroimaging biomarkers, and candidate blood predictors such as plasma APOE4 and neurofilament light chain levels.","[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk](https://eprints.whiterose.ac.uk)  \nUniversities of Leeds, Sheffield and York  \nDeposited via The University of Sheffield.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/id/eprint/237298/](https://eprints.whiterose.ac.uk/id/eprint/237298/)  \nVersion: Published Version  \nArticle:  \nHuang, Y. , Zhang, H. , Ma, B. et al. (2026) Converse or reverse? Machine-learning modeling for disease progression: A study based on Alzheimer’s disease continuum  \ncohort. NeuroImage, 327. 121754. ISSN: 1053-8119  \n[https://doi.org/10.1016/j.neuroimage.2026.121754](https://doi.org/10.1016/j.neuroimage.2026.121754)  \n© 2026 The Author(s) . Published by Elsevier Inc. This is an open access article under the CC BY-NC license ([http://creativecommons.org/l](http://creativecommons.org/l)icenses/bync/4.0/).  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC) licence. This licence allows you to remix, tweak, and build upon this work non-commercially, and any new works must also acknowledge the authors and be non-commercial. You don’t have to license any derivative works on the same terms. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \nNeuroImage 327 (2026) 121754  \nContents lists available at ScienceDirect  \nNeuroImage  \njournal [homepage:](homepage: www.elsevier.com/locate/ynimg)[ www.elsevier.com/locate/ynimg](homepage: www.elsevier.com/locate/ynimg)  \n| Converse or reverse? Machine-learning modeling for disease progression: A study based on Alzheimer’s disease continuum cohort\u003Cbr>Yujing Huang (黄玉晶)a,g,h,1,*, Hao Zhang (张灏)a,1, Buqing Ma (马步青)a, Zhe Yu (俞哲)a, Shenyi Dai (戴珅懿)e, Lu Cheng (程璐)f, Li Su (苏里)c,d, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Gaoyi Yang (杨高怡)a,*, Qingguo Ma (马庆国)b,**\u003Cbr>a Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University, Hangzhou 310024 Zhejiang Province, China\u003Cbr>b Laboratory of Neuromanagement, Zhejiang University, Hangzhou 310024 Zhejiang Province, China c Sheffield Institute of Translational Neuroscience, University of Sheffield, Sheffield S102TN, United Kingdom d Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, United Kingdom\u003Cbr>e China Jiliang University, Hangzhou 310024 Zhejiang Province, China f Hangzhou Dianzi University, Hangzhou 310024 Zhejiang Province, China\u003Cbr>g Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, Center for Infectious Disease Research, School of Medicine, Westlake University, Hangzhou 310024 Zhejiang Province, China\u003Cbr>h Westlake University Research Center for Industries of the Future, Westlake University, Hangzhou 310024 Zhejiang Province, China |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Machine learning Random Forest\u003Cbr>Healthy-MCI-AD continuum ADNI |  | Introduction: Longitudinal trajectories from healthy aging to Mild Cognitive Impairment and Alzheimer’s Disease involve complex mechanisms.\u003Cbr>Methods: We evaluated five machine learning approaches (Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, Convolutional Neural Network) to assess the importance of potential predictive markers across the health-to-dementia continuum. Using the ADNI cohort across four phases (ADNI1, ADNIGO, ADNI2, ADNI3), we analyzed participants with distinct trajectories: stable, convertible, and reverse progression.\u003Cbr>Results: Random Forest outperformed other models across key effectiveness metrics and achieve","cbCaitCiwm9SQX89","https://ap.wps.com/l/cbCaitCiwm9SQX89","pdf",3260004,1,19,"English","en",105,"# Introduction\n# Methods\n# Results\n## Model performance and key predictors\n# Conclusion","[{\"question\":\"Which machine-learning models were compared in the study?\",\"answer\":\"Five approaches were evaluated: Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, and Convolutional Neural Network.\"},{\"question\":\"How did Random Forest perform compared with other models?\",\"answer\":\"Random Forest outperformed the other models across key effectiveness metrics, achieving a macroaveraged sensitivity of 70.8% and specificity of 96.8% across participant groups.\"},{\"question\":\"What kinds of predictors were identified for disease trajectory classification?\",\"answer\":\"The model emphasized visuospatial and memory-related cognitive dysfunction, amyloid-related neuroimaging biomarkers, and blood predictors including plasma APOE4 and neurofilament light chain levels.\"}]","Converse or reverse? Machine-learning modeling for disease progression - A study based on Alzheimer’s disease continuum cohort | PDF",1785938964,48,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"converse-or-reverse-machine-learning-modeling-for-disease-progression-a-study-based-on-alzheimers-disease-continuum-cohort","",{"@graph":36,"@context":86},[37,54,69],{"@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/converse-or-reverse-machine-learning-modeling-for-disease-progression-a-study-based-on-alzheimers-disease-continuum-cohort/127454/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which machine-learning models were compared in the study?","Question",{"text":76,"@type":77},"Five approaches were evaluated: Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, and Convolutional Neural Network.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How did Random Forest perform compared with other models?",{"text":81,"@type":77},"Random Forest outperformed the other models across key effectiveness metrics, achieving a macroaveraged sensitivity of 70.8% and specificity of 96.8% across participant groups.",{"name":83,"@type":74,"acceptedAnswer":84},"What kinds of predictors were identified for disease trajectory classification?",{"text":85,"@type":77},"The model emphasized visuospatial and memory-related cognitive dysfunction, amyloid-related neuroimaging biomarkers, and blood predictors including plasma APOE4 and neurofilament light chain levels.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},"General","general"]