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Extraction of Diagnostic Rules using Recursive Partitioning Systems: A Comparision of Two Approaches
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
1992 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 4, no 5, 373-387 p.Article in journal (Refereed) Published
Abstract [en]

There are several empirical systems based on principles of learning from examples that can be used as a tool for decision support by medical experts in medicine. We are comparing two systems of this kind, one based on Quinlan's ID3 algorithm, and the other based on Breiman's CART (Classification And Regression Trees) algorithm. Both of these methods represent the extracted knowledge in form of binary tree structured diagnostic rules. In this paper we present the most important features of the two systems and discuss important differences between the two; all this in a uniform framework. We then study the implications these differences and similarities make when applied to clinical data. The empirical study includes two medical data sets: the first one concerning patients with highly selective vagotomy (HSV) for duodenal ulcer surgery, and the second one concerning patients with non-specified liver disease.

Place, publisher, year, edition, pages
1992. Vol. 4, no 5, 373-387 p.
Keyword [en]
Inductive learning systems, Assistant professional, CART, Tree classifiers methodology, Medical evaluation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-95647DOI: 10.1016/0933-3657(92)90021-GOAI: oai:DiVA.org:liu-95647DiVA: diva2:637108
Available from: 2013-07-16 Created: 2013-07-16 Last updated: 2017-12-06

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