Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome

The metabolic syndrome is a set of risk factors that include abdominal obesity, insulin resistance, dyslipidemia and hypertension. It has affected around 25% of adults in the US and become a serious problem in Asian countries recently due to the change in dietary habit and life style. On the other hand, Bayesian networks that are the models to solve the problems of uncertainty provide a robust and transparent formalism for probabilistic modeling, so they have been used as a method for diagnostic or prognostic model in medical domain. Since the K2 algorithm, a well-known algorithm for Bayesian networks structure learning, is influenced by an input order of the attributes, an optimization of BN attribute ordering has been studied as a research issue. This paper proposes a novel ordering optimization method using a genetic algorithm based on medical expert knowledge in order to solve this problem. For experiments, we use the dataset examined twice in 1993 and 1995 in Yonchon County of Korea. It has 18 attributes of 1193 subjects participated in both surveys. Using this dataset, we make the prognostic model of the metabolic syndrome using Bayesian networks with an optimized ordering by evolutionary approach. Through an ordering optimization, the prognostic model of higher performance is constructed, and the optimized Bayesian network model by the proposed method outperforms the conventional BN model as well as neural networks and k-nearest neighbors. Finally, we present the application program using the prognostic model of the metabolic syndrome in order to show the usefulness of the proposed method.