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Bayes Multistage Classifier and Boosted C4.5 Algorithm in Acute Abdominal Pain Diagnosis

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Man-Machine Interactions

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 59))

Abstract

The medical decision problem – acute abdominal pain diagnosis is presented in the paper. We use two methods of classification, which are based on a decision tree scheme. The first of them generates classifier only based on learning set. It is boosted C4.5 algorithm. The second approach is based on Bayes decision theory. This decision algorithm utilizes expert knowledge for specifying decision tree structure and learning set for determining mode of decision making in each node. The experts-physicians gave the decision tree for performing Bayes hierarchical classifier.

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Author information

Authors and Affiliations

  1. Chair of Systems and Computer Networks, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Robert Burduk & Michał Woźniak

Authors
  1. Robert Burduk
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  2. Michał Woźniak
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Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Krzysztof A. Cyran , Stanisław Kozielski , Urszula Stańczyk  & Alicja Wakulicz-Deja , ,  & 

  2. University of Manitoba, Winnipeg, Canada

    James F. Peters

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© 2009 Springer-Verlag Berlin Heidelberg

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Burduk, R., Woźniak, M. (2009). Bayes Multistage Classifier and Boosted C4.5 Algorithm in Acute Abdominal Pain Diagnosis. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_39

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  • DOI: https://doi.org/10.1007/978-3-642-00563-3_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00562-6

  • Online ISBN: 978-3-642-00563-3

  • eBook Packages: EngineeringEngineering (R0)

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