Analysis of Coronary Heart Disease Risk Factors and Construction of Disease Prediction Models

Wang Chenglong; He Xinye; Li Yanqi; Wang Shu; Dong Liguang; Wang Shuyu; Zhang Hongye; Zhang Yuqing; Zhou Xianliang; Liu Lisheng; Hu Aihua

doi:10.3969/j.issn.1674-4055.2023.12.12

Abstract：Objective To explore the importance of coronary artery disease(CAD)risk factors and construct the CAD prediction model.Methods Two thousand seven hundred thirteen participants were enrolled from May to July 2014 at Peking University Shougang Hospital,including 345 CAD cases and 2368 control cases.Logistic Regression(LR)and Random Forest(RF)were used to construct the prediction model,and the hyperparameters were optimized using grid cross-validation.Results In the CAD group,age,BMI,blood pressure(BP),pulse wave velocity(PWV),serum creatinine(Scr,),and fasting plasma glucose(FPG,P＜0.01)levels were higher than those in the control group.The duration of diabetes,hypertension,hyperlipidemia(P＜0.01),arterial ultrasound(P＜0.01),and fatigue(P＜0.01)were more severe in the CAD group.The levels of low-density lipoprotein cholesterol(LDL-C,P＜0.01),high-density lipoprotein cholesterol(HDL-C,P＜0.01),and sleep duration(P＜0.01)were lower in the CAD group.The LR and RF,machine learning algorithms produced CAD risk prediction models with a high area under the curve(AUC)and specificity but low sensitivity before adjusting the parameters.After tuning the parameters,the AUC value changed little,and the sensitivity increased significantly.The top 10 essential risk factors for both CAD risk models were age,duration of hyperlipidemia,duration of diabetes,fatigue,Scr,and PWV.Conclusion Five risk factors of Age,Fatigue Frequency,Scr,and PWV,Positive arterial ultrasound,and duration of hyperlipidemia prevalence strongly influence the construction of CAD prediction models.Class-imbalanced disease datasets enable the construction of clinical prediction models by hyperparameter-tuned machine-learning algorithms.