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Computer-Aided Sequential Diagnosis Using Fuzzy Relations – Comparative Analysis of Methods

  • Marek Kurzynski
  • Andrzej Zolnierek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

Abstract

A specific feature of the explored diagnosis task is the dependence between patient’s states at particular instants, which should be taken into account in sequential diagnosis algorithms. In this paper methods for performing sequential diagnosis using fuzzy relation in product of diagnoses set and fuzzified feature space are developed and evaluated. In the proposed method first on the base of learning set fuzzy relation is determined as a solution of appropriate optimization problem and next this relation in the form of matrix of membership grade values is used at successive instants of sequential diagnosis process. Different algorithms of sequential diagnosis which differ with as well the sets of input data as procedure are described. Proposed algorithms were practically applied to the computer-aided recognition of patient’s acid-base equilibrium states where as an optimization procedure genetic algorithm was used. Results of comparative experimental analysis of investigated algorithms in respect of classification accuracy are also presented and discussed.

Keywords

Feature Space Decision Algorithm Fuzzy Relation Membership Grade Soft Decision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Kurzynski, M.W.: Benchmark of Approaches to Sequential Diagnosis. In: Lisboa, P., Ifeachor, J., Szczepaniak, P.S. (eds.) Perspectives in Neural Computing, pp. 129–140. Springer, Heidelberg (1998)Google Scholar
  2. 2.
    Kurzynski, M.W.: Multistage Diagnosis of Myocardial Infraction Using a Fuzzy Relation. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1014–1019. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Kurzynski, M.W., Zolnierek, A.: A Recursive Classifying Decision Rule for Second- Order Markov Chains. Control and Cybernetics 18, 141–147 (1990)MathSciNetGoogle Scholar
  4. 4.
    Zolnierek, A.: The Empirical Study of the Naive Bayes Classifier in the Case of Markov Chain Recognition Task. In: Kurzynski, M.W., Wozniak, M. (eds.) Computer Recognition Systems CORES 2005, pp. 329–336. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Devroye, L., Gyorfi, P., Lugossi, G.: A Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996)zbMATHGoogle Scholar
  6. 6.
    Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley and Sons, New York (2001)zbMATHGoogle Scholar
  7. 7.
    Czogala, E., Leski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Springer, Heidelberg (2000)zbMATHGoogle Scholar
  8. 8.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Adison-Wesley, New York (1989)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Marek Kurzynski
    • 1
    • 2
  • Andrzej Zolnierek
    • 1
    • 2
  1. 1.Faculty of Electronics, Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland
  2. 2.The Witelon University of Applied SciencesLegnicaPoland

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