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A Model of a Diagnostic Rule in the Dempster-Shafer Theory

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

In the paper a model of a rule in medical diagnosis is proposed. The Dempster-Shafer theory of evidence and fuzzy sets are implemented in the rule representation. The basic probability assignment describes certainty of the rule. Fuzzy sets model the rule premises. The diagnosis is indicated by the belief and the plausibility measures. Thresholds are used to adjust the significance of the rules and quality of observations. The suggested methods are verified for databases of thyroid gland diseases: the database found in the Internet and individually gathered data, as well as simulated data and the iris plants database.

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References

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Author information

Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, 16 Akademicka St., 44 -100, Gliwice, Poland

    Ewa Straszecka

Authors
  1. Ewa Straszecka
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

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© 2006 Springer-Verlag Berlin Heidelberg

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Straszecka, E. (2006). A Model of a Diagnostic Rule in the Dempster-Shafer Theory. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_105

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  • DOI: https://doi.org/10.1007/11785231_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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