Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data

Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a priori selected input features and thus might suffer from poor... View More