[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123612-en":3,"doc-seo-123612-105":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":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},123612,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese - A Longitudinal Study","Machine learning is explored as a means to predict cognitive impairment and to identify important factors underlying impairment in Chinese middle-aged and older adults. The study analyzes questionnaire data from 2,326 participants and incorporates baseline physical examination, biomarker, functional, demographic, and emotional measures across follow-ups at Years 2 and 4. Random forest models are compared with logistic regression for longitudinal prediction and validated using cross-sectional Year 4 data.","International Journal of Public Health ORIGINAL ARTICLE  \npublished: 19 January 2023  \ndoi: 10.3389/ijph.2023.1605322  \n\n|  |  |  |\n| --- | --- | --- |\n|  |  |  |\n| Using Machine Learning to Predict |  |  |\n| Cognitive Impairment Among |  |  |\n| Middle-Aged and Older Chinese: A |  |  |\n| Longitudinal Study |  |  |\n| Haihong Liu 1,2, Xiaolei Zhang 3,4, Haining Liu 2,5,6* and Sheau Tsuey Chong 1, 7* |  |  |\n| 1Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan |  |  |\n| Malaysia, Bangi, Malaysia, 2Department of Psychology, Chengde Medical University, Chengde, China, 3Department of |  |  |\n| Biomedical Engineering, Chengde Medical University, Chengde, China, 4Faculty of Engineering, Universiti Putra Malaysia, |  |  |\n| Serdang, Malaysia, 5Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, China, 6Hebei |  |  |\n| International Research Center of Medical Engineering, Chengde Medical University, Chengde, China, 7Counselling Psychology |  |  |\n| Programme, Secretariat of Postgraduate Studies, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, |  |  |\n| Bangi, Malaysia |  |  |\n| Objective: To explore the predictive value of machine learning in cognitive impairment, |  |  |\n| and identify important factors for cognitive impairment. |  |  |\n|  | Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and |  |\n| Edited by:\u003Cbr>Gabriel Gulis,\u003Cbr>University of Southern Denmark,\u003Cbr>Denmark | physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was |  |\n| Reviewed by: applied to establish a prediction model and verify its longitudinal prediction accuracy for |  |  |\n| Alessandra Costanza,\u003Cbr>University of Geneva, Switzerland\u003Cbr>*Correspondence:\u003Cbr>Haining Liu | cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. |  |\n| [liuhn0401@sina.com](liuhn0401@sina.com)\u003Cbr>Sheau Tsuey Chong | Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, |  |\n| [stchong@ukm.edu.my](stchong@ukm.edu.my) and cross-sectional Year 4 [AUC = 0 .81, 0 .79, 0 .80] compared with logistic regression |  |  |\n| This Original Article is part of the IJPH\u003Cbr>Special Issue “Public Health and\u003Cbr>Primary Care, is 1+1=1?” | [AUC = 0 .61, 0 .62, 0 .70] . Baseline physical examination (e.g. , BMI, Blood pressure), biomarkers (e.g. , cholesterol), functioning (e.g. , functional limitations), demography (e.g. , age), and emotional status (e.g., depression) characteristics were identified as the top ten |  |\n| Received: 15 August 2022\u003Cbr>Accepted: 09 January 2023\u003Cbr>Published: 19 January 2023 | important predictors of cognitive impairment.\u003Cbr>Conclusion: ML algorithms could enhance the prediction of cognitive impairment among |  |\n| Citation: the middle-aged and older Chinese for 4 years and identify essential risk markers. Liu H, Zhang X, Liu H and Chong ST |  |  |\n| (2023) Using Machine Learning to Keywords: longitudinal study, machine learning, random forest, middle-aged and older Chinese, cognitive |  |  |\n| Predict Cognitive Impairment Among impairment, dementia |  |  |\n| Middle-Aged and Older Chinese: A |  |  |\n| Longitudinal Study.  \u003Cbr>Int J Public Health 68:1605322. Abbreviations: ADL, activities of daily living; CHARLS, China health and retirement longitudinal study; IADL, instrumental |  |  |\n| doi: 10.3389/ijph.2023.1605322 activities of daily living; ML, machine learning; TICS, telephone interview of cognitive status. |  |  |\n\nInt J Public Health | Owned by SSPH+ | Published by Frontiers 1 January 2023 | Volume 68 | Article 1605322  \nINTRODUCTION  \nWith the curre","cbCaijsmOejBfjBx","https://ap.wps.com/l/cbCaijsmOejBfjBx","pdf",704423,1,11,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Introduction\n## Background on dementia and aging\n## Cognitive impairment screening\n## Rationale for prediction models","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"The study explores the predictive value of machine learning for cognitive impairment and identifies important factors related to cognitive impairment.\"},{\"question\":\"What data and modeling approach are used to make predictions?\",\"answer\":\"Questionnaire and physical examination evaluations are collected at baseline with follow-ups at Years 2 and 4, and a random forest machine learning model predicts cognitive impairment at both follow-up points.\"},{\"question\":\"How do random forest and logistic regression compare in performance?\",\"answer\":\"Random forest shows high accuracy for outcomes at Year 2 and Year 4, and its longitudinal prediction performance is compared against logistic regression using AUC values.\"}]","Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese - A Longitudinal Study | PDF",1785817631,28,{"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},"using-machine-learning-to-predict-cognitive-impairment-among-middle-aged-and-older-chinese-a-longitudinal-study","",{"@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/using-machine-learning-to-predict-cognitive-impairment-among-middle-aged-and-older-chinese-a-longitudinal-study/123612/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of this study?","Question",{"text":75,"@type":76},"The study explores the predictive value of machine learning for cognitive impairment and identifies important factors related to cognitive impairment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and modeling approach are used to make predictions?",{"text":80,"@type":76},"Questionnaire and physical examination evaluations are collected at baseline with follow-ups at Years 2 and 4, and a random forest machine learning model predicts cognitive impairment at both follow-up points.",{"name":82,"@type":73,"acceptedAnswer":83},"How do random forest and logistic regression compare in performance?",{"text":84,"@type":76},"Random forest shows high accuracy for outcomes at Year 2 and Year 4, and its longitudinal prediction performance is compared against logistic regression using AUC values.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]