[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122420-en":3,"doc-seo-122420-105":30,"detail-sidebar-cat-0-en-105":95},{"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},122420,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity - a cross-sectional study","An explainable machine learning model is developed and validated to predict the risk of sleep disorders in older adults living with multimorbidity. A cross-sectional cohort of 471 participants is recruited between October and November 2024, using sociodemographic, health behavior, mental health, and disease-related data. Six ML methods are compared, with GBM showing the best discrimination (AUC 0.881). SHAP explanations identify seven key predictors—frailty, cognitive status, nutritional status, living alone, depression, smoking, and anxiety—supporting clinical interpretability.","TYPE Original Research PUBLISHED 11 August 2025  \nDOI 10.3389/fpubh.2025.1619406  \nOPEN ACCESS  \nEDITED BY  \nSurapati Pramanik,  \nNandalal Ghosh B.T. College, India  \nREVIEWED BY  \nKuldeep Kumar,  \nBond University, Australia Sharker Md. Numan,  \nBangladesh Open University, Bangladesh Rola Angga Lardika,  \nRiau University, Indonesia  \n*CORRESPONDENCE  \nRu Gao  \n [154475957@qq.com](154475957@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 28 April 2025  \nACCEPTED 28 July 2025  \nPUBLISHED 11 August 2025  \nCORRECTED 29 August 2025  \nCITATION  \nWang X, Zhang D, Lu L, Meng S, Li Y, Zhang R, Zhou J, Yu Q, Zeng L, Zhao J, Zeng Y and Gao R (2025) Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study.  \nFront. Public Health 13:1619406 .  \ndoi: 10.3389/fpubh.2025.1619406  \nCOPYRIGHT  \n© 2025 Wang, Zhang, Lu, Meng, Li, Zhang, Zhou, Yu, Zeng, Zhao, Zeng and Gao. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDevelopment and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a  \ncross-sectional study  \nXia Wang 1†, Dan Zhang 2†, Liu Lu3, Shujie Meng 1, Yong Li4, Rong Zhang4, Jingjie Zhou 5, Qian Yu3, Li Zeng3, Jiang Zhao4, Yu Zeng4 and Ru Gao 6*  \n1School of Basic Medical Sciences and School of Nursing, Chengdu University, Chengdu, China,  \n2 Rehabilitation Department, Sichuan Provincial People’s Hospital East Sichuan Hospital and Dazhou First People’s Hospital, Dazhou, China, 3 Nursing Department, The Fourth People’s Hospital of Yibin, Yibin, China, 4 Rehabilitation College, Sichuan Health Rehabilitation Vocational College, Zigong, China, 5Tellyes Scientific Inc., Tianjin, China, 6 Nursing Department, The People’s Hospital of Wenjiang Chengdu, Chengdu, China  \nObjective: To develop and validate an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity. Methods: A total of 471 older adults with multimorbidity were recruited between October and November 2024. We employed six machine learning (ML)  \nmethods, namely logistic regression (LR), neural network (NN), support vector machine (SVM), gradient boosting machine (GBM), K-Nearest Neighbors (KNN), and light gradient boosting machine (LightGBM), to predict the risk of sleep disorders based on their sociodemographic data, health behavior factors, mental health, and disease-related data. The optimal model was identified through the evaluation of the area under the curve (AUC) . This study also employed explainable machine learning techniques to provide insights into the model’s predictions and outcomes using the SHAP (Shapley Additive Explanations) approach.  \nResults: The prevalence of sleep disorders was 28.7% . Among the six models developed, the GBM model achieved the best performance with an AUC of 0.881. The analysis of feature importance revealed that the top seven predictors of sleep disorders were frailty, cognitive status, nutritional status, living alone, depression, smoking status, and anxiety.  \nConclusion: This study is the first to predict sleep disorders in Chinese older adults with multimorbidity using explainable machine learning methods and to identify seven significant risk factors. The SHAP method enhances the interpretability of machine learning models and helps medical staff better understand the rationale behind the predicted outcomes more effectively.  \nKEYWORDS  \nmachine learning, multimorbidi","cbCaimdRjdxnBDEh","https://ap.wps.com/l/cbCaimdRjdxnBDEh","pdf",1406975,1,15,"English","en",105,"# Introduction\n## Background: population aging and multimorbidity\n## Sleep disorders in older adults and associated risks\n# Objective\n# Methods\n## Participants and data collection\n## Machine learning models and evaluation\n## Explainability using SHAP\n# Results\n## Prevalence and model performance\n## Feature importance and key predictors\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To develop and validate an explainable machine learning model that predicts the risk of sleep disorders in older adults with multimorbidity.\"},{\"question\":\"Which data types are used to train the prediction models?\",\"answer\":\"Sociodemographic data, health behavior factors, mental health measures, and disease-related data are used for prediction.\"},{\"question\":\"Which model performed best and what was its AUC?\",\"answer\":\"The GBM model achieved the best performance with an AUC of 0.881 among the six tested models.\"},{\"question\":\"How does the study explain the model’s predictions?\",\"answer\":\"The SHAP (Shapley Additive Explanations) approach is used to interpret feature contributions and provide insights into prediction outcomes.\"}]","Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity - a cross-sectional study | PDF",1785810536,38,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"development-and-validation-of-an-explainable-machine-learning-model-for-predicting-the-risk-of-sleep-disorders-in-older-adults-with-multimorbidity-a-cross-sectional-study","",{"@graph":36,"@context":89},[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/development-and-validation-of-an-explainable-machine-learning-model-for-predicting-the-risk-of-sleep-disorders-in-older-adults-with-multimorbidity-a-cross-sectional-study/122420/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To develop and validate an explainable machine learning model that predicts the risk of sleep disorders in older adults with multimorbidity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data types are used to train the prediction models?",{"text":80,"@type":76},"Sociodemographic data, health behavior factors, mental health measures, and disease-related data are used for prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what was its AUC?",{"text":84,"@type":76},"The GBM model achieved the best performance with an AUC of 0.881 among the six tested models.",{"name":86,"@type":73,"acceptedAnswer":87},"How does the study explain the model’s predictions?",{"text":88,"@type":76},"The SHAP (Shapley Additive Explanations) approach is used to interpret feature contributions and provide insights into prediction outcomes.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]