Cardiovascular deceases are leading cause for mortality in the world. More people are dying from these deceases rather than all other deceases. Health insurance fund in Macedonia defined a method for early detection and prevention of CVD according to risk assessment for patients CVD ailment. We analyze this method, and using machine learning algorithms, as well as comparison of international recommendations, we propose a new improved method for CVD Risk management, such as: smaller target population for better patient management, better risk factor classification and correlation, definition of high (and low) risk factors, treatment recommendations and goals.
Machine Learning, Cardiovascular Deceases Risc Factors, Data Mining