Abstract
Cardiovascular disease (CVD) represents a major cause of death around the world. Predicting incidence of CVD allows interventions in order to change lifestyle or prescribe medication. Current approaches to evaluating CVD risk use regression equations based on large data sets, but such data may not accurately reflect risks based on the individual, or specific groups. In addition, the regression equations require complete recording of clinical data which may be missing or inaccurate. This paper outlines an approach that uses a fuzzified ontology to attempt to both improve prediction of CVD and provide personalized predictive capacity.
