Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS

作     者：ZHENG Zhi Chang YUAN Wei WANG Nian JIANG Bo MA Chun Peng AI Hui WANG Xiao NIE Shao Ping ZHENG Zhi Chang;YUAN Wei;WANG Nian;JIANG Bo;MA Chun Peng;AI Hui;WANG Xiao;NIE Shao Ping

作者机构：Cardiovascular Medicine DepartmentBeijing Bo’ai HospitalChina Rehabilitation Research CenterCapital Medical UniversityBeijing 100068China Center for Coronary Artery DiseaseBeijing Anzhen HospitalCapital Medical UniversityBeijing 100029China Respiratory Medicine DepartmentBeijing Friendship Hospital Affiliated of Capital Medical UniversityBeijing 100050China School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Cardiologythe First Hospital of QinhuangdaoHebei Medical UniversityQinhuangdao 066001HebeiChina 

出 版 物：《Biomedical and Environmental Sciences》 (生物医学与环境科学（英文版）)

年 卷 期：2023年第36卷第7期

页      面：625-634页

核心收录：

学科分类：100208[医学-临床检验诊断学] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1002[医学-临床医学] 10[医学] 

基　　金：supported by Beijing Nova Program[Z201100006820087] National Key R&D Program of China[2020YFC2004800] National Natural Science Foundation of China The Capital Health Research and Development of Special Fund[2018-1-2061] The Natural Science Foundation of Beijing,China 

主　　题：Machine learning MACEs Chest pain Suspected NSTE-ACS 

摘      要：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 *** Enrolled chest pain patients were from two centers,Beijing Anzhen Emergency Chest Pain Center Beijing Bo’ai Hospital,China Rehabilitation Research *** classifiers were used to develop ML ***,Precision,Recall,F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring ***,ML model constructed by Naïve Bayes was employed to predict the occurrence of *** 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 ***,according to ROC curves and AUC,ML model constructed by different classifiers performed better than HEART scoring system only in prediction for *** 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(***≤0.78),with AUC from 0.88 to 0.98 for predicting any event,AMI and revascularization(***≤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 *** Compared with HEART risk scoring system,the superiority of ML method was demonstrated when employing Linear SVC classifier,Naïve Bayes and *** method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.