Abstract
In many medical decision problems there exist dependencies between subsequent diagnosis of the same patient. Among the different concepts and methods of using “contextual” information in pattern recognition, the approach through Bayes compound decision theory is both attractive and efficient from the theoretical and practical point of view. Paper presents the probabilistic approach (based on expert rules and learning set) to the problem of recognition of state of acid-base balance and to the problem of computer-aided anti-hypertension drug therapy. The quality of obtained classifier are compared to the frquencies of correct classification of three neural nets.
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References
Devijver, P., Kittler, J.: Pattern Recognition- A Statistical Approach. Prentice Hall, London (1982)
Haralick, R.M.: Decision Making in Context. IEEE Trans. on Pattern Anal. Machine Intell., PAMI-5 (1983)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, Chichester (2001)
Giakoumakis, E., Papakonstantiou, G., Skordalakis, E.: Rule-based systems and pattern recognition. Pattern Recognition Letters 5 (1987)
Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A.: Different approaches to the sequential diagnosis problem: a comparative study. Computers in Medicine 1, 220–225 (1997)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Lin, T.Y., Wildberger, A. (red.), Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery, San Diego, Simulation Councils Inc. (1995)
Xu, L., Krzyżak, A., Suen, C.Y.: Methods of Combining Multiple Classifiers and Their Applications to Handwritting Recognition. IEEE Transactions on Systems, Man, and Cybernetics 22(3), 418–435 (1992)
Ji, C., Ma, S.: Combination of Weak Classifiers. IEEE Transaction on Neural Networks 8(1), 32–42 (1997)
Kittler, J., Alkoot, F.M.: Sum versus Vote Fusion in Multiple Clasifier Systems. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(1), 110–115 (2003)
Lam, L., Suen, C.Y.: Application of Majority Voting to Pattern Recognition: An Analysis of Its Bechavior and Performance. IEEE Transaction on Systems, Man, and Cybernetics- Part A: Systems and Humans 27(5), 553–567 (1997)
Puchala, E.: A Bayes Algorithm for the Multitask Pattern Recognition Problem – direct approach. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J., Zomaya, A.Y. (eds.) ICCS 2003. LNCS, vol. 2659, pp. 3–10. Springer, Heidelberg (2003)
Burduk, R.: Decision Rules for Bayesian Hierarchical Classifier with Fuzzy Factor. Soft Methodology and Random Information Systems. In: Advances in Soft Computing, pp. 519–526. Springer, Berlin (2004)
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Wozniak, M. (2005). Markov Chains Pattern Recognition Approach Applied to the Medical Diagnosis Tasks. 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_24
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DOI: https://doi.org/10.1007/11573067_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29674-4
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