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Bayesian networks, rule induction and logistic regression in the prediction of women survival suffering from breast cancer

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Progress in Artificial Intelligence (EPIA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1323))

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Abstract

In this paper we present an empirical comparison between several paradigms coming from Statistics and Artificial Intelligence for solving a supervised classification problem. The empirically compared paradigms are Bayesian Networks, Rule Induction and Logistic Regression. The problem to tackle is the prediction of women survival diagnosed with breast cancer taking into account four predictor variables gathered at the moment of the diagnosis. The data file includes 1000 diagnosed cases at the Oncological Institute of Gipuzkoa (Basque Country). The validation of the paradigms was carried out using the 10-fold cross-validation method.

This work was supported by the Diputación Foral de Gipuzkoa, under grant OF 92/1996, by the grant UPV 140.226-EA186/96 from the University of the Basque Country, and by the grant PI 95/52 from the Gobierno Vasco — Departamento de Educación, Universidades e Investigación.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Spain

    P. Larrañaga, M. J. Gallego & B. Sierra

  2. Department of Economics, University of the Basque Country, Spain

    L. Urkola

  3. Oncological Institute of Gipuzkoa, Spain

    M. J. Michelena

Authors
  1. P. Larrañaga
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  2. M. J. Gallego
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  3. B. Sierra
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  4. L. Urkola
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  5. M. J. Michelena
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Editor information

Ernesto Coasta Amilcar Cardoso

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© 1997 Springer-Verlag Berlin Heidelberg

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Larrañaga, P., Gallego, M.J., Sierra, B., Urkola, L., Michelena, M.J. (1997). Bayesian networks, rule induction and logistic regression in the prediction of women survival suffering from breast cancer. In: Coasta, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 1997. Lecture Notes in Computer Science, vol 1323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0023932

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  • DOI: https://doi.org/10.1007/BFb0023932

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63586-4

  • Online ISBN: 978-3-540-69605-6

  • eBook Packages: Springer Book Archive

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