Evaluation of Machine Learning Methods to Predict Coronary Artery Disease Using Metabolomic Data
This work addresses the need for accurate, non-invasive, and low-cost disease prediction methods for healthcare applications, but it is incremental as it applies existing methods to a specific domain without introducing new techniques.
The study tackled the problem of predicting coronary artery disease using metabolomic data by systematically evaluating supervised machine learning methods like L1 regression and random forest classifiers, comparing them to traditional regression-based approaches to potentially improve accuracy.
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.