Ensemble Machine Learning and Its Validation for Prediction of Coronary Artery Disease and Acute Coronary Syndrome Using Focused Carotid Ultrasound

The objective of this study is to demonstrate the effectiveness of ensemble-learning-driven machine learning (EML) algorithms over the conventional ML (CML) algorithms in predicting cardiovascular events (CVEs) such as coronary artery disease (CAD) and acute coronary syndrome (ACS). Furthermore, this study demonstrates the improvement in overall CVE prediction by including carotid ultrasound image phenotypes in the feature set. The methodology consisted of collecting and amalgamating 24 risk predictors along with coronary angiography as the gold standard. We further hypothesize that for such a fused set of predictors, EML systems can perform better compared with CML systems. The EML system design consisted of risk predictors for each of 459 participants undergoing baseline characteristics and independent K10-based training model generation based on three sets of algorithms: seven CML, three homogeneous ensemble ML (EML-homo), and five heterogeneous ensemble ML (EML-hetro). These training models were then applied to the test patients to predict CAD and ACS. As part of the performance, the AUC for EML-homo improved over CML by 4.3% for CAD and 1.1% for ACS, respectively. Similarly, the AUC for EML-hetro improved over CML by 3.23% for CAD and 2.11% for ACS, respectively. The proposed EML-based system was validated against the two validation databases consisting of 303 and 522 participants for CAD and ACS. We thus conclude that the EML-based algorithms are better in predicting CAD and ACS, compared with CML, proving our hypothesis.