  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 poorer accuracy.
Supervised machine learning methods can potentially be used in order to fully
exploit the dimensionality and richness of the data. In this paper, we
systematically implement and evaluate a set of supervised learning methods (L1
regression, random forest classifier) and compare them to traditional
regression-based approaches for disease prediction using metabolomic data.
