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

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

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PAOLA BRITOS

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

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RAMÓN GARCÍA-MARTÍNEZ

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

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https://doi.org/10.1142/S0129183106008558Cited by:7 (Source: Crossref)

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.

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References

W. Fritzet al., Robot. Auton. Syst. 5, 109 (1989), DOI: 10.1016/0921-8890(89)90004-3. Crossref, Google Scholar
R. García-Martínez and D. Borrajo, J. Intell. Robot. Syst. 29, 47 (2000), DOI: 10.1023/A:1008134010576. Crossref, Web of Science, Google Scholar
G. Perichinsky and R. García-Martínez, Proc. Workshop Comput. Sc. Researchers (La Plata University Press, Buenos Aires, 2000) p. 107. Google Scholar
G. Perichinsky, R. García-Martínez and A. Proto, Knowledge Discovery Based on Computational Taxonomy And Intelligent Data Mining, CD of the VI Comput. Sc. Argentinean Congr. (Ushuaia, 2000) . Google Scholar
G. Perichinsky et al. , Data Mining: Supervised and Non-Supervised Intelligent Knowledge Discovery , Proc. II Workshop Computes Sc. Researchers ( San Luis University Press , San Luis , 2001 ) . Google Scholar
G. Perichinskyet al., Taxomic Evidence Applying Algorithms of Intelligent Data Minning Asteroid Families, Proc. Int. Conf. Comput. Sci., Software Eng., Information Technology, e-Bussines & Applications p. 308. Google Scholar
M. Chen, J. Han and P. Yu, IEEE Trans. Knowledge and Data Eng. 8, 866 (1996), DOI: 10.1109/69.553155. Crossref, Web of Science, Google Scholar
H. Mannila , Methods and problems in data mining , Proc. of Int. Conf. on Database Theory . Google Scholar
G. Piatetski-Shapiro , W. J. Frawley and C. J. Matheus , Knowledge Discovery in Databases: An Overview ( AAAI-MIT Press , Menlo Park, California , 1991 ) . Google Scholar
S. Evangelos and J. Han , Proc. 2nd Int. Conf. Knowledge Discovery and Data Min. . Google Scholar
R. S. Michalski , I. Bratko and M. Kubat , Machine Learning and Data Mining, Methods and Applications ( John Wiley & Sons Ltd , West Sussex, England , 1998 ) . Google Scholar
R. S. Michalski , J. G. Carbonell and T. M. Mitchell , Machine learning I: An AI Approach ( Morgan Kaufmann , Los Altos, CA , 1983 ) . Crossref, Google Scholar
M. Holsheimer and A. Siebes, Data mining: The search for knowledge in databases, Report CS-R9406 (University of Amsterdam, Amsterdam, 1991) . Google Scholar
R. S. Michalski , A. B. Baskin and K. A. Spackman , A logic-based approach to conceptual database analysis , 6th Annu. Symp. Comput. Appli. Med. Care ( 1982 ) . Google Scholar
G. Piatetsky-Shapiro , U. M. Fayyad and P. Smyth , From Data Mining to Knowledge Discovery ( AAAI Press/MIT Press , CA , 1996 ) . Google Scholar
J. Han , Data Mining, Urban and Dasgupta ( Encyclopedia of Distributed Computing, Kluwer Academic Publishers , 1999 ) . Google Scholar
R. Cowell et al. , Probabilistic Networks and Expert Systems ( Springer , New York , 1990 ) . Google Scholar
M. Ramoni and P. Sebastiani , Bayesian methods in Intelligent Data Analysis: An Introduction ( Physica Verlag , Heidelberg , 1999 ) . Google Scholar
J. Pearl , Probabilisic Reasoning in Intelligent Systems ( Morgan Kaufmann , San Mateo , 1988 ) . Google Scholar
I. A. Beinlich et al. , The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks , Proc. 2nd Eur. Conf. Arti. Intell. Medicine . Google Scholar
T. W. Bickmore, Real-Time Sensor Data Validation, NASA Contractor Report 195295 (National Aeronautics and Space Administration, United States, 1994) . Google Scholar
J. S. Breese and R. Blake, Automating Computer Bottleneck Detection with Belief Nets, Proc. Conf. Uncertainty Arti. Intell. p. 33. Google Scholar
K. J. Ezawa and T. Schuermann, Fraud/uncollectible debt detection using a Bayesian network based learning system: A rare binary outcome with mixed data structures, Proc. Conf. Uncertainty Arti. Intell. p. 157. Google Scholar
D. Heckerman, M. Chickering and D. Geiger, Machine learning 20, 197 (1995). Web of Science, Google Scholar
F. Diaz and J. M. Corchado , Rough sets bases learning for Bayesian networks , International workshop on objetive Bayesian methodology ( 1999 ) . Google Scholar
D. Heckerman, A tutorial on learning Bayesian networks, Technical report MSR-TR-95-06 (Microsoft research, Redmond, 1995) . Google Scholar
D. Heckerman and M. Chickering, Efficient approximation for the marginal likelihood of incomplete data given a Bayesian network, Technical report MSR-TR-96-08 (Microsoft Research, Microsoft Corporation, 1996) . Google Scholar
M. Ramoni and P. Sebastiani, Learning Bayesian networks from incomplete databases, Technical report KMI-TR-43 (Knowledge Media Institute, The Open University, 1996) . Google Scholar
P. M. Murphy and D. W. Aha, UCI Repository of machine learning databases, Machine-readable data repository, Department of Information and Computer Science (University of California, Irvine) . Google Scholar
A. Carbajo, S. Curto and N. Schweigmann, Distribución espacio-temporal de Aedes aegypti (Diptera: Culicidae). Su relación con el ambiente urbano y el riesgo de transmisión del virus dengue en la Ciudad de Buenos Aires, Departamento de Ecología, Genética y Evolución, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires (2003) . Google Scholar