Risk assessment for primary coronary heart disease event using dynamic bayesian networks
Date Issued
January 1, 2015
DOI
10.1007/978-3-319-19551-3_20
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.