Predicting Coronary Heart Disease risk using health risk assessment data

Almost 15% of global deaths in 2008 were attributed to Coronary Heart Disease (CHD). While major risk factors for CHD are widely known, lack of comprehensive data on relevant risk factors has limited the ability to predict risk of developing CHD in large populations. In this study, we explore the application of the Framingham Risk Model to predict CHD risk using a limited set of attributes present in a health risk assessment (HRA) dataset from a digital health company. HRAs often fail to capture all the needed attributes of the Framingham Model, such as LDL and HDL cholesterol values that significantly affect CHD risk. Hence, we enhance our analysis with the National Health and Nutrition Examination Survey (NHANES) data from the Centers for Disease Control (CDC), the United States public health agency. Our preliminary findings indicate that HRA data can be successfully used as input for the Framingham Risk Model in predicting risk of CHD utilizing NHANES data to predict missing attributes, thus extending the use of HRAs for disease risk prediction.