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.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
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)
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)
Kurzynski, M.W., Zolnierek, A.: A Recursive Classifying Decision Rule for Second- Order Markov Chains. Control and Cybernetics 18, 141–147 (1990)
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)
Devroye, L., Gyorfi, P., Lugossi, G.: A Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996)
Duda, R., Hart, P., Stork, D.: Pattern Classification. John Wiley and Sons, New York (2001)
Czogala, E., Leski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. Springer, Heidelberg (2000)
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Adison-Wesley, New York (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kurzynski, M., Zolnierek, A. (2005). Computer-Aided Sequential Diagnosis Using Fuzzy Relations – Comparative Analysis of Methods. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_25
Download citation
DOI: https://doi.org/10.1007/11573067_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
Online ISBN: 978-3-540-31658-9
eBook Packages: Computer ScienceComputer Science (R0)