Risk Assessment for Primary Coronary Heart Disease Event Using Dynamic Bayesian Networks

Coronary heart disease (CHD) is the leading cause of mortality worldwide. Primary prevention of CHD denotes limiting a first CHD event in individuals who have not been formally diagnosed with the disease. This paper demonstrates how the integration of a Dynamic Bayesian network (DBN) and temporal abstractions (TAs) can be used for assessing the risk of a primary CHD event. More specifically, we introduce basic TAs into the DBN nodes and apply the extended model to a longitudinal CHD dataset for risk assesment. The obtained results demonstrate the effectiveness of our proposed approach.