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Combined Classification of Risk Factors for Appendicitis Prediction in Childhood

  • Conference paper
Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 384))

Abstract

Abdominal pain is a common symptom associated with transient disorders or serious disease. Diagnosing the cause of abdominal pain can be difficult, because many diseases can cause this symptom. One of the most common conditions associated with acute abdominal pain is acute appendicitis. Diagnosis is based on patient history and physical examination. The present study is based on a data set consisting of 516 children’s medical records. Each record consists of 15 factors that are used in the routine clinical practice for the assessment of the acute appendicitis. The importance of these factors is examined in this paper, with the use of many Artificial Intelligence and classification methods. As a result, only 5 factors of the initial 15 factors can be used, in order to have equal or even better diagnosis.

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Author information

Authors and Affiliations

  1. Cultural Technology and Communication Department, University of the Aegean, 81100, Lesvos, Greece

    Theodoros Iliou & Christos-Nikolaos Anagnostopoulos

  2. Hellenic Telecommunication Organization S.A. (OTE), 99 Kifissias Avenue, GR-151 24, Athens, Greece

    Ioannis M. Stephanakis

  3. Medical Informatics Lab. Medical School, Democritus University of Thrace, 68100, Alexandroupolis, Greece

    George Anastassopoulos

Authors
  1. Theodoros Iliou
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  2. Christos-Nikolaos Anagnostopoulos
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  3. Ioannis M. Stephanakis
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  4. George Anastassopoulos
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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Iliou, T., Anagnostopoulos, CN., Stephanakis, I.M., Anastassopoulos, G. (2013). Combined Classification of Risk Factors for Appendicitis Prediction in Childhood. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_22

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  • DOI: https://doi.org/10.1007/978-3-642-41016-1_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41015-4

  • Online ISBN: 978-3-642-41016-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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