Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest ...Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.展开更多
基金supported by Beijing Nova Program[Z201100006820087]National Key R&D Program of China[2020YFC2004800]+2 种基金National Natural Science Foundation of China[81870322]The Capital Health Research and Development of Special Fund[2018-1-2061]The Natural Science Foundation of Beijing,China[7191002].
文摘Objective We aimed to assess the feasibility and superiority of machine learning(ML)methods to predict the risk of Major Adverse Cardiovascular Events(MACEs)in chest pain patients with NSTE-ACS.Methods Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research Center.Five classifiers were used to develop ML models.Accuracy,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system.Ultimately,ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.Results According to learning metrics,ML models constructed by different classifiers were superior over HEART(History,ECG,Age,Risk factors,&Troponin)scoring system when predicting acute myocardial infarction(AMI)and all-cause death.However,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI.Among the five ML algorithms,Linear support vector machine(SVC),Naïve Bayes and Logistic regression classifiers stood out with all Accuracy,Precision,Recall and F-Measure from 0.8 to 1.0 for predicting any event,AMI,revascularization and all-cause death(vs.HEART≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(vs.HEART≤0.85).ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome(ACS),abnormal electrocardiogram(ECG),elevated hs-cTn I,sex and smoking were risk factors of MACEs.Conclusion Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and Logistic.ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.
