Risk assessment for primary coronary heart disease event using dynamic bayesian networks
Date Issued
January 1, 2015
Abstract
Coronary heart disease (CHD) is the leading cause of mortality worldwide. Primary prevention ofCHDdenotes limiting a firstCHDevent 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 primaryCHDevent. More specifically, we introduce basic TAs into the DBN nodes and apply the extended model to a longitudinal CHDdataset for risk assesment. The obtained results demonstrate the effectiveness of our proposed approach.