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The medical diagnostic support system using extended Rough Neural Network and Multiagent

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Abstract

Multiagent technologies enable us to explore their sociological and psychological foundations. Amedical dignostic support system is built using this. Moreover, We think that the data inputted can acquire higher diagnostic accuracy by sorting out using a determination table. In this paper, the recurrence diagnostic system of cancer is built and the output error of Multiagents learning method into the usual Neural Network and a Rough Neural Network and Genetic Programming be compared. The data of the prostates cancer offered by the medical institution and a renal cancer was used for verification of a system.

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Author information

Authors and Affiliations

  1. Department of Electric and Information Faculty of Engineering, Toin University of Yokohama, 1614 Kurogane-cho, Aoba-ku, Yokohama, 225-8502, Japan

    Daisuke Yamaguchi, Fumiyo Katayama & Muneo Takahashi

  2. Hitachi Advanced Systems Corporation, Yokohama, Japan

    Masataka Arai

  3. Department of Information Systems, Tokyo University of Information Sciences, Chiba, Japan

    Kenneth J. Mackin

Authors
  1. Daisuke Yamaguchi
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  2. Fumiyo Katayama
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  3. Muneo Takahashi
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  4. Masataka Arai
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  5. Kenneth J. Mackin
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Corresponding author

Correspondence to Daisuke Yamaguchi.

Additional information

This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008

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Yamaguchi, D., Katayama, F., Takahashi, M. et al. The medical diagnostic support system using extended Rough Neural Network and Multiagent. Artif Life Robotics 13, 184–187 (2008). https://doi.org/10.1007/s10015-008-0543-3

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  • DOI: https://doi.org/10.1007/s10015-008-0543-3

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