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An Efficient Attribute Ordering Optimization in Bayesian Networks for Prognostic Modeling of the Metabolic Syndrome

  • Han-Saem Park
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

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.

Keywords

Genetic Algorithm Metabolic Syndrome Waist Circumference Bayesian Network Abdominal Obesity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Mykkanen, L., Kuusisto, J., Pyorala, K., Laakso, M.: Cardiovascular Disease Risk Factors as Predictors of Type 2 (Non-Insulin-Dependent) Diabetes Mellitus in Elderly Subjects. Diabetologia 50, 453–469 (2004)Google Scholar
  2. 2.
    Mehta, N.N., Reilly, M.P.: Mechanisms of the Metabolic Syndrome. Drug Discov. Today 1(2), 187–194 (2004)CrossRefGoogle Scholar
  3. 3.
    Lee, S.M., Abbott, P.A.: Bayesian Networks for Knowledge Discovery in Large Datasets: Basics for Nurse Researchers. J. Biomed. Inform. 36, 389–399 (2003)CrossRefGoogle Scholar
  4. 4.
    Antal, P., Fannes, G., Timmerman, D., Moreau, Y., Moor, B.D.: Using Literature and Data to Learn Bayesian Networks as Clinical Models of Ovarian Tumors. Artif. Intell. Med. 30, 257–281 (2004)CrossRefGoogle Scholar
  5. 5.
    Wang, X.-H., Zheng, B., Good, W.F., King, J.L., Chang, Y.-H.: Computer-Assisted Diagnosis of Breast Cancer Using a Data-driven Bayesian Belief Network. Int. J. Med. Inform. 54, 115–126 (1999)CrossRefGoogle Scholar
  6. 6.
    Sierra, B.: Using Bayesian Networks in the Construction of A Bi-level Multi-Classifier. A Case Study Using Intensive Care Unit Patients Data. Artif. Intell. Med. 22, 233–248 (2001)Google Scholar
  7. 7.
    Larranaga, P., Kuijpers, C.M.H., Murga, R.H., Yurramendi, Y.: Learning Bayesian Network Structures by Searching for the Best Ordering with Genetic Algorithms. IEEE T. Syst. Man Cy. A 26(4) (1996)Google Scholar
  8. 8.
    Moon, M.K., Cho, Y.M., Lim, K.S., Park, S., Lee, H.K.: Metabolic Syndrome. The Korean Society of Endocrinology 18, 105–116 (2003)Google Scholar
  9. 9.
    Lindblad, U., Langer, R.D., Wingard, D.L., Thomas, R.G., Barrett-Connor, E.L.: Metabolic Syndrome and Ischemic Heart Disease in Elderly Men and Women. Am. J. Epidemiol. 153, 481–489 (2001)CrossRefGoogle Scholar
  10. 10.
    Girod, J.P., Brotman, D.J.: The Metabolic Syndrome as a Vicious Cycle: Does Obesity Beget Obesity. Med. Hypotheses 60(4), 584–589 (2003)CrossRefGoogle Scholar
  11. 11.
    Cooper, G.F., Herskovits, E.A.: A Bayesian Method for Induction of Probabilistic Networks from Data. Mach. Learn. 9(4), 309–347 (1992)MATHGoogle Scholar
  12. 12.
    Park, Y.: Prevalence of Diabetes and IGT in Yonchon County. South Korea Diabetes Care 18, 545–548 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Han-Saem Park
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

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