10-year CVD risk prediction and minimization via InverseClassification

Cardiovascular diseases (CVD) remain the leading cause of death around the world. In past decades, many preventive strategies have been recommended to reduce the risk of CVD. However, current CVD risk prediction schemes are not targeted to personalized and optimized recommendations. The goal of this study was to better identify individuals at high risk of a CVD event, and recommend an optimal set of risk factor changes that could reduce the risk of long-term CVD events. We identified 100 demographic, lab, lifestyle, and medication variables for 12907 individuals who participated to the ARIC study and had no CVD events at baseline. We examined the prognostic performance of these features in isolation and ranked them based on mutual information. Then we combined those features to build predictive models using k-nearest neighbor prediction to estimate the 10-year CVD risk for each individual. Our feature-ranking method agreed with traditional risk factors identified by a domain expert. Our approach was successful in identifying cases with high risk and performed as well as traditional methods. Then we applied inverse classification to find the personalized optimal changes to reduce 10-year CVD risk. We also created a personalized package of five optimal changes for each individual to reduce their 10-year CVD risk. This approach can be applied to other chronic disease risk prediction and personalized recommendations, and may be useful to both health care providers and patients in making personalized health care recommendations and decisions.