PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES

Pablo Felgaer (), Paola Britos () and Ramón García-Martínez ()
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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

International Journal of Modern Physics C (IJMPC), 2006, vol. 17, issue 03, 447-455

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.

Keywords: Bayes; induction learning; classification; hybrid intelligent systems (search for similar items in EconPapers)
Date: 2006
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DOI: 10.1142/S0129183106008558

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