Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2002.9B.5.625

Bayesian Network-Based Analysis on Clinical Data of Infertility Patients  

Jung, Yong-Gyu (서울보건대학 전산정보처리과)
Kim, In-Cheol (경기대학교 전자계산학과)
Publication Information
The KIPS Transactions:PartB / v.9B, no.5, 2002 , pp. 625-634 More about this Journal
Abstract
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.
Keywords
Baysian Networks; Infertility Patients; Features Reduction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bouckaert, R., 'Bayesian Belief Networks : From Construction to Inference,' Doctoral Dissertation, University of Utrecht, The Netherlands, 1995
2 대한산부인과학회, 부인과학(개정판), 도서출판 칼빈서적, 1991
3 정혁, '불임, 무엇이 문제인가 - 그 원인과 치료', 우리출판사, 1997
4 Cheng, J., Bell, D. A. and Liu, W., 'An Algorithm for Bayesian Belief Network Construction from Data,' Proceedings of AI & STAT-97, Florida, pp.83-90, 1997
5 Cheng, J. and Greiner, R., 'Learning Bayesian Belief Network Classifiers : Algorithms and System,' Proceedings of the fourteenth Canadian conference on artificial intelligence, 2001
6 Cheng, J., 'BN PowerConstructor,' http://www.cs. ualberta.ca/~jcheng/bnsoft.htm
7 Dougherty, J., Kohavi, R., and Sahami, M., 'Supervised and Unsupervised Discretization of Continuous Features,' Proceedings of ICML-95, pp.194-202, 1995
8 Friedman, N., Linial, M., Nachman, I., Peter, D., 'Using Bayesian networks to Analyze Expression Data,' Journal of Computational Biology, 2000   DOI   ScienceOn
9 Gorrill, Marsha-J. ; Kaplan, Paul-F. ; Patton, Phillip-E. ; Burry, Kenneth-A., 'Initial Experience with Extended Culture and Blastocyst Transfer of Aryopreserved Embryos,' American Journal of Obstetric & Gynecology, Vol.180, No.6, 1999
10 Heckerman, D., 'A Tutorial on Learning Bayesian Networks,' Technical Report MSR-TR-95-06, Microsoft Research, 1995
11 Heckerman, D., Meek, C. and Cooper, G., 'A Bayesian Approach to Causal Discovery,' Technical Report MSR-TR-97-05, Microsoft Research, 1997
12 Kevin Patrick Merphy, 'A Brief Introduction to Graphical Models and Bayesian Networks,' Technical Report, Department of Computer Science, UC Berkley, 2001
13 Provan, G. M. and Singh, M., 'Learning Bayesian Networks Using Feature Selection,' Learning from Data, Lecture Notes in Statistics, Berlin : Springer-Verlag, Vol.112, pp. 291-300, 1996   DOI
14 Kohavi, R. and John G., 'Wrappers for Feature Subset Selection,' Artificial Intelligence, Special Issue on Relevance, Vol.97, No.1-2, pp.273-324, 1997   DOI   ScienceOn
15 Langley, P. and Sage, S., 'Induction of Selective Bayesian Classifiers,' Proceedings of UAI-94, 1994
16 Friedman, N., 'Learning Bayesian Networks in the Presence of Missing Values and Hidden Variables,' Proceedings of ICML-97, pp.125-133, 1997
17 Singh, M., 'Learning Bayesian Networks from Incomplete Data,' Proceedings of AAAI-97, The MIT Press, pp.534-539, 1997
18 Sprites, P., Gleymour, C, and Sceines, R., Causation, Prediction, and Search, New York : Springer-Verlag, 1993
19 Jensen, F. V., An Introduction to Bayesian Networks, New York : Springer-Verlag, 1996
20 Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, Morgan Kaufmanm. 2001
21 Tom M. Mitchael, Machine Learning, McGrow-Hill, 1997
22 Pazzani, M. J., 'Searching for Dependencies in Bayesian Classifiers,' Proceedings of AI & STAT-95, 1995
23 Pearl, J., Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmanm, 1988