Comparing three-class diagnostic tests by three-way ROC analysis

Stephan Dreiseitl, Lucila Ohno-Machado, Michael Binder

Research output: Contribution to journalArticlepeer-review

105 Citations (Scopus)

Abstract

Three-way ROC surfaces are based on a generalization of dichotomous ROC analysis to three-class diagnostic tests. The discriminatory power of three-class diagnostic tests is measured by the volume under the ROC surface. This measure can be given a probabilistic interpretation similar to the equivalence of the c- index to the area under the ROC curve. This article presents a method to calculate nonparametric estimates of the variance of the volume under the surface using Mann-Whitney U statistics. As a simple extension of this result, it is possible to calculate covariance estimates for the volume under the surface. This allows the statistical comparison of two tests used for diagnostic tasks with three possible outcomes. The formulas derived are validated on synthetic data and applied to a three- class data set of pigmented skin lesions. It is shown that a neural network algorithm trained on clinical data and lesion features performs better than one trained on only the lesion features.

Original languageEnglish
Pages (from-to)323-331
Number of pages9
JournalMedical Decision Making
Volume20
Issue number3
DOIs
Publication statusPublished - 2000
Externally publishedYes

Keywords

  • Receiver operating characteristic curves
  • Trichotomous ROC analysis
  • Humans
  • ROC Curve
  • Skin Neoplasms/diagnosis

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