Explainable AI for Digital Health: Predicting Depression Risk in Older Adults Living Alone Using Machine Learning

Background: This study aimed to evaluate the performance of machine learning models in predicting depression risk among older adults living alone and to identify the features contributing to those predictions using explainable artificial intelligence (XAI). Methods: We analysed 2022 nationwide survey data in Korea. A total of 1007 older adults remained after excluding respondents who lived in multi-person households, were aged < 65 years, or had physician-diagnosed dementia. Depression risk was defined using the CES-D-10 (cutoff ≥ 10). After removing features with high multicollinearity, logistic LASSO selected 23 predictors. Six algorithms were fitted using the training set, with hyperparameter tuning performed by 5-fold cross-validation where applicable, and evaluated in a held-out test set following a 70/30 split. SMOTE was applied only to the training data. Performance was summarised using AUC, sensitivity, specificity and the F1 score with bootstrap 95% confidence intervals, and stability was assessed by repeated stratified cross-validation. SHAP values provided explainability. Results: LightGBM achieved an AUC of 0.802 (95% CI 0.747–0.852), followed by Random Forest (0.794) and Logistic Regression (0.779). These differences were small relative to the uncertainty of the estimates. SHAP analysis identified oral health-related quality of life, satisfaction with relationships with children, frequency of social contact, overall life satisfaction, satisfaction with health status, and age as the most influential features. IADL limitations, diabetes, hypertension, and perceived social class contributed to predictions with smaller effects. Conclusions: An explainable LightGBM model achieved an AUC of 0.802 for depression risk among older adults living alone and identified psychosocial and health-related features, particularly oral health and social connectedness, that may help inform future screening strategies.