Machine learning (ML) is able to extract patterns and develop algorithms to construct data-driven models.
The Authors used ML models to gain insight into the relative importance of variables to predict obstructive coronary artery disease (CAD) using the Coronary Computed Tomographic Angiography for Selective Cardiac Catheterization (CONSERVE) study, as well as to compare prediction of obstructive CAD to the CAD consortium clinical score (CAD2). They further perform ML analysis to gain insight into the role of imaging and clinical variables for revascularization.
For obstructive CAD, the ML model outperformed CAD2. BMI is an important variable, although currently not included in most scores.