  Current clinical practice guidelines for managing Coronary Artery Disease
(CAD) account for general cardiovascular risk factors. However, they do not
present a framework that considers personalized patient-specific
characteristics. Using the electronic health records of 21,460 patients, we
created data-driven models for personalized CAD management that significantly
improve health outcomes relative to the standard of care. We develop binary
classifiers to detect whether a patient will experience an adverse event due to
CAD within a 10-year time frame. Combining the patients' medical history and
clinical examination results, we achieve 81.5% AUC. For each treatment, we also
create a series of regression models that are based on different supervised
machine learning algorithms. We are able to estimate with average R squared =
0.801 the time from diagnosis to a potential adverse event (TAE) and gain
accurate approximations of the counterfactual treatment effects. Leveraging
combinations of these models, we present ML4CAD, a novel personalized
prescriptive algorithm. Considering the recommendations of multiple predictive
models at once, ML4CAD identifies for every patient the therapy with the best
expected outcome using a voting mechanism. We evaluate its performance by
measuring the prescription effectiveness and robustness under alternative
ground truths. We show that our methodology improves the expected TAE upon the
current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The
algorithm performs particularly well for the male (24.3% improvement) and
Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive
interface, providing physicians with an intuitive, accurate, readily
implementable, and effective tool.
