Construction and validation of an interpretable machine learning model for predicting diabetes risk in COPD patients

To construct and validate a machine learning (ML) model for predicting diabetes risk in COPD patients, enabling early and personalized intervention. Using data from the MIMIC-IV database, 49 variables were screened by LASSO and logistic regression. Six ML algorithms were constructed and internally validated on a 70%/30% split dataset. Model performance was assessed using multiple metrics, followed by external validation. Interpretability was achieved through SHAP analysis. All six ML algorithms demonstrated strong performance across training, testing, and validation sets. The LightGBM model achieved the best overall performance (AUC = 0.87). Feature importance analysis identified glucose, chronic kidney disease, and hyperlipidemia as the top three most important features for diabetes development in COPD patients. An interpretable ML-based risk prediction model for diabetes in COPD patients was constructed and validated. The LightGBM-based tool shows potential for supporting early personalized care and improving prognosis.