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A comparison of logistic regression to decision tree induction in the diagnosis of carpal tunnel syndrome.

Authors
  • Rudolfer, S M
  • Paliouras, G
  • Peers, I S
Type
Published Article
Journal
Computers and biomedical research, an international journal
Publication Date
Oct 01, 1999
Volume
32
Issue
5
Pages
391–414
Identifiers
PMID: 10529299
Source
Medline
License
Unknown

Abstract

This paper aims to compare and contrast two types of model (logistic regression and decision tree induction) for the diagnosis of carpal tunnel syndrome using four ordered classification categories. Initially, we present the classification performance results based on more than two covariates (multivariate case). Our results suggest that there is no significant difference between the two methods. Further to this investigation, we present a detailed comparison of the structure of bivariate versions of the models. The first surprising result of this analysis is that the classification accuracy of the bivariate models is slightly higher than that of the multivariate ones. In addition, the bivariate models lend themselves to graphical analysis, where the corresponding decision regions can easily be represented in the two-dimensional covariate space. This analysis reveals important structural differences between the two models.

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