ArticleBMC public health2025
Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Article
- Machine learning in mental health promotion for older adults: a scoping review.BMC geriatrics · 2026Article
- Development and validation of an explainable machine learning-based risk prediction model for obesity in Chinese children and adolescents: a population-based study.Frontiers in nutrition · 2026Article
- Predicting social isolation in maintenance hemodialysis patients using machine learning methods: a cross-sectional study.Frontiers in psychiatry · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
introductionThis study aimed to develop a machine learning system to predict social isolation risk in older adults.
methodsData from a sample of 6588 older adults in China were analyzed using information from China Health and Retirement Longitudinal Study from 2015 to 2018. We employed the light gradient boosting machine (Lightgbm) algorithm to determine the most common predictors of social isolation among older adults. After identifying these predictors, we trained and optimized 7 models to predict the risk of social isolation among older adults: Lightgbm, logistic regression, decision tree, support vector machine, random forest, gradient boosting decision tree (Gbdt), and Xgboost. In addition, the Shapely additive explanation (SHAP) method was used to show the contribution of each social isolation predictor to the prediction. Statistical analysis was conducted from December 2023 to April 2024.
resultsThe Gbdt model had the best performance with an accuracy of 0.7247, sensitivity of 0.9207, specificity of 0.6273, F1 score of 0.6894, and Area Under Curve of 0.84. In addition, the SHAP method demonstrated that intergeneration financial support, child visits, age, left-hand grip strength, and loneliness were the most important characteristics.
conclusionsThe combination of Gbdt and SHAP provides a clear explanation of the factors contributing to predicting the personalized risk of social isolation for individuals and an intuitive understanding of the impact of key features.
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