A 4-variable logistic regression model (pain, address, sleep time, and grip-max) demonstrated robust predictive performance for depression risk in heart disease patients, achieving an AUC of 0.788.
Observational (n=947)
Yes
Can a machine learning-based model accurately predict depression risk in middle-aged and elderly Chinese patients with heart disease?
A simplified 4-variable logistic regression model using pain, address, sleep time, and grip strength can accurately predict depression risk in middle-aged and elderly Chinese patients with heart disease.
Effect estimate: AUC 0.788
Objective Heart disease is a leading cause of death and disability among middle-aged and elderly populations. Depression is a common comorbidity that impairs prognosis and quality of life. This study aimed to develop a machine learning (ML)-based depression risk prediction model based on China Health and Retirement Longitudinal Study (CHARLS) data. Methods A total of 947 middle-aged and elderly heart disease patients from CHARLS 2015 were included after applying missing data criteria. Missing values were filled using random forest (RF), and data were split 7:3 into training and validation cohorts. Variables selection in the training cohort using univariate analysis, Lasso regression, recursive feature elimination (RFE), and feature importance evaluation using RF and decision tree (DT). Variables appearing in at least three of these five methods were selected. Eleven ML models were constructed and evaluated by area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis. Five-fold cross-validation enhanced stability and SHapley Additive exPlanation (SHAP) values interpreted feature importance. Results Fifty-eight variables were extracted. After multi-step variable selection within the training cohort, nine variables (address, grip-max, arthritis rheumatism, Hope, sleep time, pain, Retire, ADL, IADL) were initially identified. Among 11 ML models, the logistic regression (LR) algorithm demonstrated the best overall performance with an AUC of 0.792 in the validation cohort. A 4-variable LR model (pain, address, sleep time, and grip-max) was optimized, achieving a comparable AUC of 0.788. SHAP analysis confirmed pain as the most critical predictor (69.0% of depressed patients reported pain versus 26.9% of non-depressed patients). Rural residence (86.5% vs. 66.7%), shorter sleep time (median 5.25(4.00, 7.00) vs. 6.00(5.00, 8.00) hours), and lower grip-max (24.50(20.00, 30.00) vs. 27.00(22.50, 33.40) increased depression risk. A user-friendly web-based calculator was developed for clinical applications. Conclusions The simplified LR model exhibits robust predictive performance and clinical applicability for assessing high depression risk in middle-aged and elderly patients with heart disease.
Fu et al. (2026) conducted an observational in Heart disease with depression risk (n=947). Machine learning-based depression risk prediction model (Logistic Regression) was evaluated on Area under the curve (AUC) for depression risk prediction (AUC 0.788). A 4-variable logistic regression model (pain, address, sleep time, and grip-max) demonstrated robust predictive performance for depression risk in heart disease patients, achieving an AUC of 0.788.