Abstract
The present paper is devoted to the pattern recognition procedures that simultaneously use the information contained in the empirical data (learning set) and the set of expert rules with unprecisely formulated weights understood as conditional probabilities. Adopting the probabilistic model the combined and unified recognition algorithms are derived. In the first approach algorithm is based simply on the both set of data, in the second however, one set of data is transformed into the second one. Proposed algorithms were applied practically to the diagnosis of acute renal failure in children. Obtained results have proved its effectiveness in the computer medical decision-making.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Devroye L., Gyorfi L., Lugosi G.: A Probabilistic Theory of Pattern Recognition. Springer Verlag (1996)
Pearl J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufman Publishers, Inc., San Francisco (1992)
Dubois D., Lang J.: Possibilistic Logic, In: Handbook of Logic in Artificial Intelligence and Logic Programming. Oxford Univ. Press (1994) 439–513
Neapolitan R.: Probabilistic Reasoning in Expert Systems. Wiley, New York (1990)
Kurzynski M., Sas J., Blinowska A.: Rule-Based Medical Decision-Making with Learning. Proc. 12th World IFAC Congress, Vol. 4, Sydney (1993) 319–322
Kurzynski M., Sas J.: Rule-Based Classification Procedures Related to the Unprecisely Formulated Expert Rules. Proc. SIBIGRAPI Conference, Rio de Janeiro (1998) 241–245
Sachs L.: Applied Statistics. A Handbook of Techniques. Springer Verlag, New York, Berlin, Tokyo (1984)
James A. J.: Renal Diseases in Childhood. The C.V.Mosby Co., Saint Louis (1976)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kurzynski, M., Puchala, E., Sas, J. (2001). Hybrid Pattern Recognition Algorithms with the Statistical Model Applied to the Computer-Aided Medical Diagnosis. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_20
Download citation
DOI: https://doi.org/10.1007/3-540-45497-7_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42734-6
Online ISBN: 978-3-540-45497-7
eBook Packages: Springer Book Archive