Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm

作者：Dandan Tang, Fengwei Liang, Xingli Gu, Yuanyuan Jin, Xiaohan Hu, Liu Fen, Yining Yang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-11142-3 · 被引用次数：8 · 研究领域：Artificial Intelligence in Healthcare、Diabetes, Cardiovascular Risks, and Lipoproteins、Cardiovascular Function and Risk Factors

T2DM is a major risk factor for CHD. In recent years, machine learning algorithms have demonstrated significant advantages in improving predictive accuracy; however, studies applying these methods for clinical prediction and diagnosis of CHD-DM2 remain limited. This study aims to evaluate the performance of machine learning models and to develop an interpretable model to identify critical risk factors of CHD-DM2, thereby supporting clinical decision-making. Data were collected from cardiovascular inpatients admitted to the First Affiliated Hospital of Xinjiang Medical University between 2001 and 2018. A total of 12,400 patients were included, comprising 10,257 cases of CHD and 2143 cases of CHD-DM2.To address the class imbalance in the dataset, the SMOTENC algorithm was applied in conjunction with the themis package for data preprocessing. Final predictors were identified through a combined approach of univariate analysis and Lasso regression. We then developed and validated seven machine learning models: Logistic, Logistic_Lasso, KNN, SVM, XGBoost, RF, and LightGBM. The predictive performance of the five models was compared using evaluation metrics including accuracy, sensitivity, specificity, AUC, ROC and DCA. Additionally, SHAP values were employed to provide interpretability of the model outputs. The dataset was split into a training set (n = 8460) and a validation set (n = 3680) at a 7:3 ratio. A total of 25 predictive variables were ultimately identified through Lasso r...