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Machine learning-based predictive model for sleep disorders in diabetic patients: data analysis from CHARLS.

Maoqin Tian¹, Shunli Zuo², Yongping Sun³

¹Orthopedics Department of Guizhou Province People's Hospital, Auiyang, Guizhou Province, People's Republic of China.

Scientific Reports

|June 18, 2026

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Machine learning accurately predicts sleep disorders in diabetic patients. The extreme gradient boosting model showed the best performance, offering a valuable tool for early risk identification and management in diabetes care.

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Diabetes Management

Background:

Sleep disorders are common in patients with diabetes mellitus, significantly impacting their health.
Effective prediction of sleep disorders is crucial for timely intervention and management in diabetic populations.

Purpose of the Study:

To evaluate the effectiveness of machine learning (ML) models in predicting sleep disorders among individuals with diabetes.
To identify key predictors for sleep disorders in diabetic patients using ML techniques.

Main Methods:

Utilized data from the China Health and Retirement Longitudinal Study (CHRLS) database, including 1276 diabetic patients.
Selected six key features using single-factor correlation analysis and LASSO regression: family history of diabetes, education, marital status, chronic diseases, chronic pain, and depression.

Keywords:

CHARLS Diabetes Machine learning Prediction model Sleep disorders

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Developed and compared five ML models: logistic regression, decision tree, extreme gradient boosting (XGBoost), support vector machine, and light gradient boosting machine.

Main Results:

The extreme gradient boosting (XGBoost) model achieved the highest predictive performance, with an Area Under the Curve (AUC) of 0.850.
Calibration curves and decision curve analysis confirmed the XGBoost model's good fit, accuracy, and clinical utility.
LASSO regression identified six significant predictors contributing to sleep disorder risk in diabetic patients.

Conclusions:

Machine learning models, particularly extreme gradient boosting, are highly effective for predicting the risk of sleep disorders in diabetic patients.
The identified key predictors and the performance of the XGBoost model offer a promising approach for early detection and personalized management strategies.