title: A comparison of logistic regression to decision tree induction in the diagnosis of carpal tunnel syndrome.
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.
authors.affiliation: Mathematics Department, University of Manchester, Manchester, England.
authors.forename: S M
authors.initials: SM
authors.lastname: Rudolfer
authors.forename: G
authors.initials: G
authors.lastname: Paliouras
authors.forename: I S
authors.initials: IS
authors.lastname: Peers
mesh_terms: D000465:Algorithms; D002349:Carpal Tunnel Syndrome / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005069:Evaluation Studies as Topic; D006801:Humans; D016015:Logistic Models; D015999:Multivariate Analysis