[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-427621-105":59,"doc-detail-427621-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","comparative-assessment-of-predictive-models-for-older-adults-health-outcomes-logistic-regression-lasso-ml-llm-abstract","COMPARATIVE ASSESSMENT OF PREDICTIVE MODELS FOR OLDER ADULTS’ HEALTH OUTCOMES: LOGISTIC REGRESSION, LASSO, ML, LLM - Abstract","","Accurate prediction of health outcomes in older adults is crucial for tailoring interventions and informing public health strategies. This study compares four predictive approaches—logistic regression, LASSO regularization, machine learning ensembles (decision trees, random forests, support vector machines), and AI LLMs (DeepSeek and GPT)—for depression and mortality. Using nationally representative data from HRS and CHARLS, it assesses performance via accuracy, precision, recall, F1-score, and ROC-AUC, examines consistency across outcomes, and evaluates fairness across geographic, gender, and socioeconomic subpopulations with statistical parity, equalized odds, and predictive parity to identify trade-offs among complexity, accuracy, and fairness.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparative-assessment-of-predictive-models-for-older-adults-health-outcomes-logistic-regression-lasso-ml-llm-abstract/427621/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparative-assessment-of-predictive-models-for-older-adults-health-outcomes-logistic-regression-lasso-ml-llm-abstract/427621.png","ImageObject",300,407,{"name":92,"@type":93},"Rhys","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which predictive methods are compared for older adults’ health outcomes?","Question",{"text":112,"@type":113},"The study compares logistic regression, LASSO regularization, machine learning ensembles (decision trees, random forests, support vector machines), and AI LLMs using DeepSeek and GPT.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What health outcomes are evaluated in the comparison?",{"text":117,"@type":113},"Depression and mortality are the two critical health outcomes assessed across models.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the study measure both accuracy and fairness?",{"text":121,"@type":113},"Model performance is evaluated with accuracy, precision, recall, F1-score, and ROC-AUC. Fairness is examined by checking predictive performance disparities across subpopulations using statistical parity, equalized odds, and predictive parity.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427621,1790718816,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},687207024643,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Innovation in Aging, 2025, Vol. 9, No. S2 657  \nAbstract citation ID: igaf122.2244  \nCOMPARATIVE ASSESSMENT OF PREDICTIVE  \nMODELS FOR OLDER ADULTS’ HEALTH OUTCOMES: LOGISTIC REGRESSION, LASSO, ML, LLM  \nFeng Wei, Yuanhao Liu, and Jiayu Zheng, Johns Hopkins University, Baltimore, Maryland, United States  \nAccurate prediction of health outcomes in older adults is crucial for tailoring interventions and informing public health strategies. Despite advancements in predictive methodologies, significant gaps remain in understanding how these models perform across various health indicators and demographic segments. This study evaluates four predictive methodologies—conventional logistic regression, regularization represented by LASSO, machine learning ensembles (decision trees, random forests, support vector machines), and AI LLM (DeepSeek and GPT)—focusing on two critical health outcomes: depression and mortality. Using nationally representative data from the Health and Retirement Study (HRS) and the China Health and Retirement Longitudinal Study (CHARLS), we evaluate these models’ performance and fairness comparatively: (1) evaluate model performance through traditional metrics such as accuracy, precision, recall, F1-score, and ROC-AUC; (2) explore whether performance discrepancies are consistent across these health outcomes; (3) investigate fairness by examining predictive performance disparities among elderly subpopulations defined by geographic location, gender, and socioeconomic status, using fairness metrics like statistical parity, equalizedodds, and predictive parity. Through this multidimensional comparison, our study provides a comprehensive evaluation of the progression from traditional to AI-based predictive models, contributes to understanding the current capabilities and limitations in predicting elderly health outcomes, and identifies the trade-offs between model complexity, predictive accuracy, and fairness. These potential outcomes are expected to inform further research, and provide practical guidance for policymakers, educators, and healthcare practitioners in deploying equitable and effective predictive strategies for elderly health outcomes.","cbCaisIr2aUcwRI1","https://ap.wps.com/l/cbCaisIr2aUcwRI1","pdf",47409,"English","# Abstract\n## Studied predictive models\n## Outcomes and data sources\n## Performance metrics and fairness evaluation\n## Key contribution and practical implications","[{\"question\":\"Which predictive methods are compared for older adults’ health outcomes?\",\"answer\":\"The study compares logistic regression, LASSO regularization, machine learning ensembles (decision trees, random forests, support vector machines), and AI LLMs using DeepSeek and GPT.\"},{\"question\":\"What health outcomes are evaluated in the comparison?\",\"answer\":\"Depression and mortality are the two critical health outcomes assessed across models.\"},{\"question\":\"How does the study measure both accuracy and fairness?\",\"answer\":\"Model performance is evaluated with accuracy, precision, recall, F1-score, and ROC-AUC. Fairness is examined by checking predictive performance disparities across subpopulations using statistical parity, equalized odds, and predictive parity.\"}]","COMPARATIVE ASSESSMENT OF PREDICTIVE MODELS FOR OLDER ADULTS’ HEALTH OUTCOMES: LOGISTIC REGRESSION, LASSO, ML, LLM - Abstract | PDF",1790644366]