Machine Learning Prediction Model and Interpretability Analysis of Depression Risk in Patients with Chronic Kidney Disease

Patients with chronic kidney disease (CKD) frequently experience depressive symptoms, which substantially impair their quality of life. To facilitate the early identification of high-risk individuals, this study aimed to develop a predictive model for assessing depression risk among CKD patients. This study was based on data from the China Health and Retirement Longitudinal Study (CHARLS) 2018 wave, including 1777 middle-aged and elderly participants with self-reported CKD diagnosed by a physician. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D 10). A total of 29 variables were included, covering lifestyle factors, health status, comorbidities, and sociodemographic characteristics. The Elastic Net algorithm was employed to select 11 features with the highest predictive value. Seven machine learning models, including XGBoost and support vector machine (SVM), were compared, with CHARLS 2020 data used as a temporal validation set. In the multi-model comparison, XGBoost demonstrated discrimination performance comparable to logistic regression (LR), SVM, and multilayer perceptron (MLP) (DeLong test, p > 0.05). However, considering its superior calibration performance and ability to capture nonlinear interactions, XGBoost was selected as the final model. In the validation set, the model achieved an area under the curve (AUC) of 0.8017 and an accuracy of 72.39%. SHAP analysis further revealed the nonlinear effects of predictors, with life satisfaction, sleep duration, and self-rated health showing high contributions and negative associations with depression risk, whereas limitations in activities of daily living (ADL), physical pain, and digestive system diseases were significantly associated with an increased risk of depression. Overall, the risk of depression in CKD patients is influenced by multiple dimensions, including psychological cognition, quality of life, physical function, and social environment. The predictive model developed in this study may provide a valuable reference for the early screening of high-risk populations. However, its applicability to non-CKD populations requires further validation.