Extraction of diagnostic rules using recursive partitioning systems: A comparison of two approaches

doi:10.1016/0933-3657(92)90021-G

Artificial Intelligence in Medicine

Volume 4, Issue 5, October 1992, Pages 373-387

https://doi.org/10.1016/0933-3657(92)90021-G Get rights and content

Abstract

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.

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¹: Also with Faculty of Electrical and Computer Engineering, University of Ljubljana, Ljubljana, Slovenia, fax: +38 61 264 990

²: Also with Dept. of Mathematics and Informatics Conservatoire, National des Arts et Métiers, Paris, France, fax: +33 1 40 27 27 09

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Extraction of diagnostic rules using recursive partitioning systems: A comparison of two approaches

Abstract

Internat. J. Man-Machine Studies

ASSISTANT 86: A knowledge elicitation tool for sophisticated users

Review of five empirical learning systems within proposed schemata

Empirical modelling versus commonly applied data analysis techniques as used for decision support in liver disease