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dc.contributor.authorLenič, M.
dc.contributor.authorPovalej, P.
dc.contributor.authorZorman, M.
dc.contributor.authorPodgorelec, V.
dc.contributor.authorKokol, P.
dc.contributor.authorLhotská, Lenka
dc.date.accessioned2012-08-01T12:25:49Z
dc.date.available2012-08-01T12:25:49Z
dc.date.issued2003-04
dc.identifier.citationLenič, M. - Povalej, P. - Zorman, M. - Podgorelec, V. - Kokol, P. - et al.: Multimethod machine learning approach for medical diagnosing. In Proceedings of the ITAB 2003 Conference, New solutions for new challenges. Piscataway: IEEE, 2003, p. 195-198. ISBN 0-7803-7667-6.cze
dc.identifier.urihttp://hdl.handle.net/10467/12402
dc.description.abstractIn this paper we present a multimethod approach for induction of a specific class of classifiers, which can assist physicians in medical diagnosing in the case of mitral valve prolapse. Mitral valve prolapse is one of the most controversial prevalent cardiac condition and may affect up to ten percent of the population and in the worst case results in sudden death. MultiVeDec is a general framework enabling researchers to generate various intelligent tools based on machine learning. In this paper we focused on various decision tree methods, which are capable of extracting knowledge in a form closer to human perception, a feature that is very important in medical field. The experiment included classifiers with various classical single method approaches, evolutionary approaches, hybrid approaches and also our newest multimethod approach. The main concern of the latest approach is to rind a way to enable dynamic combination of methodologies to the somehow quasi unified knowledge representation. The proposed multimethod approach was capable to outperform all other tested approaches by producing classifier for diagnosing mitral valve prolapse with the highest overall and average class accuracy.eng
dc.publisherIEEEcze
dc.rights© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.eng
dc.titleMultimethod machine learning approach for medical diagnosingcze
dc.typepříspěvek z konference - elektronickýcze
dc.identifier.doi10.1109/ITAB.2003.1222509


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