Machine learning-based predictive model for sleep disorders in diabetic patients：Data Analysis from CHARLS

Background Sleep disorders are prevalent and constitute a major concern in patients with diabetes mellitus. Therefore, the aim of this study was to investigate the applicability of machine learning methods in predicting sleep disorders among diabetic patients. Methods Six relevant features were selected using single-factor correlation analysis and the LASSO algorithm. We developed and evaluated five ML models: logistic regression, decision tree, extreme gradient boosting, support vector machine, and light gradient boosting machine. Results Data from the China Health and Retirement Longitudinal Study database were utilized, with a total of 60,308 elderly individuals screened, of which 1,276 diabetic patients were included in the analysis. Of these, 777 did not develop sleep disorders, while 499 did. Fifteen statistically significant predictors were identified through single-factor analysis, and six relevant variables were determined via LASSO regression, including family history, education, marital status, chronic diseases, chronic pain, and depression. Based on these six variables, five ML models were constructed to predict the risk of sleep disorders in diabetic patients. Among these, the XGB model demonstrated superior performance, with an area under the curve of 0.838. The calibration curve indicated a good fit of the model on the development set, and decision curve analysis further confirmed the model's excellent net benefit and prediction accuracy. The overall performance of the XGB model was the best. Conclusions Our findings suggest that ML models, particularly extreme gradient boosting, offer the most effective approach for predicting the risk of sleep disorders in diabetic patients.