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Prediction In Health Domain Using Bayesian Networks Optimization Based On Induction Learning Techniques

Author

Listed:
  • PABLO FELGAER

    (Intelligent Systems Lab. School of Engineering, University of Buenos Aires, Paseo Colón 850 4th Floor, South Wing, (1063) Buenos Aires, Argentina)

  • PAOLA BRITOS

    (Software & Knowledge Engineering Center, Graduate School, Buenos Aires Institute of Technology, Av. Madero 399, (1106) Buenos Aires, Argentina)

  • RAMÓN GARCÍA-MARTÍNEZ

    (Software & Knowledge Engineering Center, Graduate School, Buenos Aires Institute of Technology, Av. Madero 399, (1106) Buenos Aires, Argentina)

Abstract

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Suggested Citation

  • Pablo Felgaer & Paola Britos & Ramón García-Martínez, 2006. "Prediction In Health Domain Using Bayesian Networks Optimization Based On Induction Learning Techniques," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 447-455.
    • Handle: RePEc:wsi:ijmpcx:v:17:y:2006:i:03:n:s0129183106008558
    DOI: 10.1142/S0129183106008558
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