Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
An adaptive binary PSO to learn bayesian classifier for prognostic modeling of metabolic syndrome
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification
Journal of Systems and Software
Hi-index | 0.00 |
The metabolic syndrome has become a significant problem in Asian countries recently due to the change in dietary habit and life style. Bayesian networks provide a robust formalism for probabilistic modeling, so they have been used as a method for prognostic model in medical domain. Since K2 algorithm is influenced by an input order of the attributes, optimization of BN attribute ordering is studied. This paper proposes an evolutionary optimization of attribute ordering in BN to solve this problem using a genetic algorithm with medical knowledge. Experiments have been conducted with the dataset obtained in Yonchon County of Korea, and the proposed model provides better performance than the comparable models.