Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data

CONCLUSION: Accurate predictions of angiographic CAD can be obtained using a set of routine laboratory markers, age, sex, and smoking status, holding the potential to limit the need for invasive diagnostic techniques. The extension analysis in the YFS demonstrated the potential of these findings in a younger population, and it confirmed applicability to atherosclerotic vascular …