ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Computational Psychiatry
This article is part of the Research TopicResearch on the Mechanisms, Interpretability, and Modeling of Mental Health Disorders with AI AssistanceView all articles
Predicting social isolation in maintenance hemodialysis patients using machine learning methods: A Cross-sectional study
Provisionally accepted
- 1Jishou University, Jishou, China
- 2Qilu Medical University, Zibo, China
- 3shanghai east hospital, Shanghai, China
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Background: 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. Method: 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). Result: 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). Conclusion: 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.
Keywords: machine learning, Maintenance hemodialysis, Predictive Modeling, random forest, risk stratification, Social Isolation
Received: 27 Dec 2025; Accepted: 31 Jan 2026.
Copyright: © 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) or licensor 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.
* Correspondence: Ying Li
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