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

Zhi Chang Zheng; Wei Yuan; Nian Wang; Bo Jiang; Chun Peng MA; Hui AI; Xiao Wang; Shao Ping Nie

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

Author: Zhi Chang ZHENG ^{1
,

2} ; Wei YUAN ³ ; Nian WANG ⁴ ; Bo JIANG ⁴ ; Chun Peng MA ⁵ ; Hui AI ² ; Xiao WANG ² ; Shao Ping NIE ²
Author Information

1. Cardiovascular Medicine Department, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Capital Medical University, Beijing 100068, China
2. Center for Coronary Artery Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
3. Respiratory Medicine Department, Beijing Friendship Hospital Affiliated of Capital Medical University, Beijing 100050, China.
4. School of Computer Science and Engineering, Beihang University, Beijing 100191, China.
5. Department of Cardiology, the First Hospital of Qinhuangdao, Hebei Medical University, Qinhuangdao 066001, Hebei, China.
Publication Type:Journal Article
Keywords: Chest pain; MACEs; Machine learning; Suspected NSTE-ACS
MeSH: Humans; Acute Coronary Syndrome/epidemiology*; Bayes Theorem; Feasibility Studies; Risk Assessment/methods*; Chest Pain/etiology*; Myocardial Infarction/diagnosis*
From: Biomedical and Environmental Sciences 2023;36(7):625-634
CountryChina
Language:English
Abstract: 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.