Heejoo Jin, Frankie Kim, Jaemo Koo, Daniel Lee, Eric Yan
Cardiovascular disease is the leading cause of death globally, and it is expected to grow to more than 23.6 million fatalities per year by 2030. Early detection and accurate diagnosis of cardiovascular disease can be the difference between life or death. Although there isn’t a cure for cardiovascular disease, methods for treating and partially reverse the disease are available through targeting and improving key risk factors. Machine Learning algorithms for predicting cardiovascular disease can improve prevention and provide critical insight for physicians to determine the correct treatment and diagnosis. We aim to find factors that are useful in predicting cardiovascular disease and compare the performance of different supervised classification algorithms.