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Data from 362 MHD patients recruited at a tertiary hospital in Shanghai are split into training and detection groups, and seven algorithms are compared. The Random Forest model yields the strongest performance (AUC=0.95), and feature importance highlights residence, heart failure, anxiety, monthly household income, age, and sleep condition as significant factors. Results support clinical risk stratification and timely, targeted interventions, with recommendations for future multicenter validation.",{"@graph":69,"@context":126},[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/predicting-social-isolation-in-maintenance-hemodialysis-patients-using-machine-learning-methods-a-cross-sectional-study/291165/",{"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/predicting-social-isolation-in-maintenance-hemodialysis-patients-using-machine-learning-methods-a-cross-sectional-study/291165.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this study on MHD patients?","Question",{"text":112,"@type":113},"To develop and validate a machine learning-based risk prediction model for social isolation in maintenance hemodialysis patients, and determine key risk factors.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were patients and machine learning models handled in the analysis?",{"text":117,"@type":113},"A total of 362 MHD patients were recruited and randomly divided into training and detection groups, and seven machine learning algorithms were compared.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model performed best and what predictors were identified?",{"text":121,"@type":113},"Random Forest was the best prediction model (AUC=0.95). 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Psychiatry 17:1776298 .  \ndoi: 10.3389/fpsyt.2026.1776298  \nCOPYRIGHT  \n© 2026 Li, Zhao and Wang. This is an 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.  \nPredicting social isolation in maintenance hemodialysis patients using machine learning methods: a cross-sectional study  \nYing Li 1†, Wenwen Zhao 2† and Boyang Wang 3*  \n1College of Sports Science, Jishou University, Jishou, Hunan, China, 2School of Nursing, Qilu Medical University, Zibo, Shandong, China, 3 Nephrology Department, Nephrology Department Shanghai East Hospital, Shanghai, China  \nBackground: This study aims to develop and validate a machine learning-based risk prediction model for social isolation in maintenance hemodialysis (MHD) patients, and at the same time determine the key risk factors.  \nMethod: 362 patients with MHD were recruited from a tertiary hospital in Shanghai and randomly divided into the training group and the detection group. We implemented and compared seven machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Resilient Network (EN), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) .  \nResult: In our MHD cohort, the incidence of social isolation was 45 . 856% . The comparative analysis shows that RF is the best prediction model (AUC = 0 . 95) . Feature importance analysis identified significant predictors: Place of residence (1.277), Heart failure (HF) (0.559), Anxiety (0.306), Monthly household income (0 . 269), Age (0 . 255) Sleep condition (0 . 138) .  \nConclusion: The prediction model based on RF has a good effect in identifying the social isolation risk of MHD patients. These findings enable clinicians to stratify high-risk populations and implement timely and targeted intervention measures, effectively reducing the risk of adverse consequences. Future multicenter studies should validate these results in larger cohorts.  \nKEYWORDS  \nmachine learning, maintenance hemodialysis, predictive modeling, random forest, risk stratification, social isolation  \nIntroduction  \nChronic kidney disease (CKD) has emerged as a significant global public health concern, with profound implications for healthcare systems worldwide (1) . According to statistics, the global estimated prevalence of chronic kidney disease (CKD) is 13.4%(2) . China has approximately 120 million patients with CKD, of which about 2 million have advanced to end-stage renal disease (ESRD) (3) . For patients progressing to ESRD, maintenance hemodialysis (MHD) serves as the primary renal replacement therapy, playing a critical role in disease management. As dialysis duration extends, psychological disturbances stemming from treatment complications and comorbidities become increasingly  \nFrontiers in Psychiatry 01 [frontiersin.org](frontiersin.org)  \nprevalent. Social isolation represents a state of detach","cbCaiusulJBEGk93","https://ap.wps.com/l/cbCaiusulJBEGk93","pdf",2358146,11,"English","# Background\n# Method\n# Result\n# Conclusion\n# Introduction","[{\"question\":\"What is the purpose of this study on MHD patients?\",\"answer\":\"To develop and validate a machine learning-based risk prediction model for social isolation in maintenance hemodialysis patients, and determine key risk factors.\"},{\"question\":\"How were patients and machine learning models handled in the analysis?\",\"answer\":\"A total of 362 MHD patients were recruited and randomly divided into training and detection groups, and seven machine learning algorithms were compared.\"},{\"question\":\"Which model performed best and what predictors were identified?\",\"answer\":\"Random Forest was the best prediction model (AUC=0.95). Important predictors included place of residence, heart failure, anxiety, monthly household income, age, and sleep condition.\"},{\"question\":\"What are the practical implications and next steps suggested?\",\"answer\":\"The model supports stratifying high-risk patients and implementing timely targeted interventions to reduce adverse consequences, and future multicenter studies should validate the findings in larger cohorts.\"}]","Predicting social isolation in maintenance hemodialysis patients using machine learning methods - a cross-sectional study | PDF",28]