The Bayesian network (BN) is a useful method for modeling healthcare issues since a BN can graphically represent causal relationships among variables and provide its probabilistic information,. In this study, we apply a BN method to hypertension occu...
The Bayesian network (BN) is a useful method for modeling healthcare issues since a BN can graphically represent causal relationships among variables and provide its probabilistic information,. In this study, we apply a BN method to hypertension occurrence analysis. This study used the National Health Insurance Corporation (NHIC) database from 2002 to 2010 which contains more than 100,000 cases of personal medical examinations in Korea. We investigate the causality for hypertension occurrence by a structure learning step, and then evaluate the performance to predict hypertension occurrence through parameter learning and inference steps. It is shown that the BN outperforms other prediction methods such as logistic regression, naive Bayes and support vector machine in terms of sensitivity. In addition, the BN has advantages in interpreting which variables affect the hypertension occurrence and how they are related to each other.