Development and validation of an interpretable machine learning model for predicting atrial fibrillation risk in middle-aged and older patients with coronary heart disease.

Background: Coronary heart disease (CHD) and atrial fibrillation (AF) frequently coexist, yet existing risk stratification tools inadequately capture the nonlinear, multidimensional determinants of AF in middle-aged and older CHD patients. This study aimed to develop and validate an interpretable machine learning-based prediction model leveraging electronic medical records (EMR) data. Methods: A retrospective cohort of 47,617 hospitalized CHD patients (January 2020-December 2025) was analyzed. After random forest imputation and least absolute shrinkage and selection operator (LASSO) screening, eight machine learning algorithms were trained and validated (7:3 split). Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), with Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) applied for interpretability. Results: LASSO regression identified 16 predictors, with pulse rate, total cholesterol, systolic blood pressure, creatinine, and triglycerides emerging as the top five contributors. XGBoost outperformed competing models, achieving AUCs of 0.867 (95% CI: 0.862-0.872) in the training set and 0.813 (95% CI: 0.802-0.823) in the validation set. Restricted cubic spline analysis revealed nonlinear dose-response relationships for multiple continuous variables. SHAP visualization quantified individualized feature contributions. LIME explanations demonstrated consistent local feature contributions at the individual level. Conclusions: This data-driven, interpretable XGBoost model enables individualized AF risk assessment in middle-aged and older CHD patients, offering a practical tool for early identification and targeted intervention in clinical practice.