According to a study published in the journal Radiology, when machine learning is combined with common heart scan, it can predict heart attacks and other cardiac events better than traditional risk models. Observing the worldwide data, heart disease is the most common and leading cause of death in both men and women, especially in the United States. Consequently, precision in risk assessment is mandatory for early intervention to say diet, exercise, drugs including cholesterol-lowering statins. In this context, CCTA (Coronary Computed Tomography Arteriography) provides with a highly detailed set of images of the heart vessels and happens to be a refining risk assessment tool. The study lead author Kevin M. Johnson, M.D., CCTA recently investigated a machine learning system which can mine the myriad details in CCTA-obtained images for a better and comprehensive prognostic picture.