Bayesian Network-Based Analysis on Clinical Data of Infertility Patients

KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 5, pp. 625-634, May. 2002
10.3745/KIPSTB.2002.9.5.625, Full Text:


In this paper, we conducted various experiments with Bayesian networks in order to analyze clinical data of infertility patients. With these experiments, we tried to find out inter-dependencies among important factors playing the key role in clinical pregnancy, and to compare 3 different kinds of Bayesian network classifiers (including NBN, BAN, GBN) in terms of classification performance. As a result of experiments, we found the fact that the most important features playing the key role in clinical pregnancy (Clin) are indication (IND), stimulation, age of female partner (FA), number of ova (ICT), and use of Wallace (ETM), and then discovered inter-dependencies among these features. And we made sure that BAN and GBN, which are more general Bayesian network classifiers permitting inter-dependencies among features, show higher performance than NBN. By comparing Bayesian classifiers based on probabilistic representation and reasoning with other classifiers such as decision trees and k-nearest neighbor methods, we found that the former show higher performance than the latter due to inherent characteristics of clinical domain. Finally, we suggested a feature reduction method in which all features except only some ones within Markov blanket of the class node are removed, and investigated by experiments whether such feature reduction can increase the performance of Bayesian classifiers.

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Cite this article
[IEEE Style]
Y. G. Jung and I. C. Kim, "Bayesian Network-Based Analysis on Clinical Data of Infertility Patients," KIPS Journal B (2001 ~ 2012) , vol. 9, no. 5, pp. 625-634, 2002. DOI: 10.3745/KIPSTB.2002.9.5.625.

[ACM Style]
Yong Gyu Jung and In Cheol Kim. 2002. Bayesian Network-Based Analysis on Clinical Data of Infertility Patients. KIPS Journal B (2001 ~ 2012) , 9, 5, (2002), 625-634. DOI: 10.3745/KIPSTB.2002.9.5.625.