Objective To develop an integrated risk model for coronary artery occlusion based on data of both traditional Chinese medicine(TCM)and western medicine data,and to evaluate the contribution of TCM-specific indicators to conventional coronary heart disease(CHD)risk prediction.Methods Data of TCM indicators(tongue,facial,and pulse diagnostics)and clinical parame-ters from patients diagnosed with CHD at the Cardiology Department of Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine,from October 3,2023 to March 15,2024,were collected.Important variables were identified using importance screen-ing and correlation analysis with CHD risk factors and laboratory markers.Six machine learn-ing models including logistic regression(LR),decision tree(DT),support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),and gradient boosting(GB),were ap-plied to evaluate the risk of coronary artery obstruction by combining clinical and TCM data of CHD.Model performance was assessed using metrics such as accuracy,precision,and re-call,with reliability validated through ten-fold cross-validation.Results A total of 288 patients were included in the study.Fifteen clinical risk factors,includ-ing body mass index(BMI),myoglobin,and alcohol consumption history,were incorporated into the diagnostic models.The KNN model showed good performance when combining clin-ical data with tongue and facial data.The SVM model performed well when clinical data was combined with pulse data.Among all the models,the KNN model with 10-fold cross-valida-tion,which integrates the three types of TCM diagnostic data(tongue,face,and pulse)with clinical data,performs the best(accuracy:0.837,precision:0.814,and recall:0.809).Conclusion Incorporating TCM diagnostic data can enhance the accuracy of coronary artery obstruction risk assessment.The KNN prediction model that integrate tongue,facial,and pulse data performs the best and can be recommended as a clinical decision support tool.
