Does Size Really Matter—Using a Decision Tree Approach for Comparison of Three Different Databases from the Medical Field of Acute Appendicitis

  • Authors:

  • Milan Zorman;Hans-Peter Eich;Bruno Stiglic;Christian Ohmann;Mitja Lenic

  • Affiliations:

  • Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia/ milan.zorman@uni-mb.si;Theoretical Surgery Unit, Department of General and Trauma Surgery, Heinrich-Heine University, Dü/sseldorf, Germany;Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia;Theoretical Surgery Unit, Department of General and Trauma Surgery, Heinrich-Heine University, Dü/sseldorf, Germany;Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2002

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Abstract

Decision trees have been successfully used for years in many medical decision making applications. Transparent representation of acquired knowledge and fast algorithms made decision trees one of the most often used symbolic machine learning approaches. This paper concentrates on the problem of separating acute appendicitis, which is a special problem of acute abdominal pain, from other diseases that cause acute abdominal pain by use of an decision tree approach. Early and accurate diagnosing of acute appendicitis is still a difficult and challenging problem in everyday clinical routine. An important factor in the error rate is poor discrimination between acute appendicitis and other diseases that cause acute abdominal pain. This error rate is still high, despite considerable improvements in history-taking and clinical examination, computer-aided decision-support, and special investigation such as ultrasound. We investigated three databases of different size with ca ses of acute abdominal pain to complete this task as successful as possible. The results show that the size of the database does not necessary directly influence the success of the decision tree built on it. Surprisingly we got the best results from the decision trees built on the smallest and the biggest database, where the database with medium size (relative to the other two) was not so successful. Despite this we were able to produce decision tree classifiers that were capable of producing correct decisions on test data sets with accuracy up to 84%, sensitivity to acute appendicitis up to 90%, and specificity up to 80% on the same test set.