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The Area under the ROC Curve and Its Competitors

Author

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  • Jørgen Hilden

Abstract

The area under the receiver operating characteristic (ROC) curve is a popular measure of the power of a (two-disease) diagnostic test, but it is shown here to be an inconsistent criterion: tests of indistinguishable clinical impacts may have different areas. A class of diagnosticity measures (DMs) of proven optimality is proposed instead. Once a regret(-like) measure of diagnostic uncertainty is agreed upon, the associated DM is uniquely defined and, indeed, calculable from the ROC curve configuration. Two scaled variants of the ROC are introduced and used to advantage in the analysis. They may also be helpful to students of medical decision making.

Suggested Citation

  • Jørgen Hilden, 1991. "The Area under the ROC Curve and Its Competitors," Medical Decision Making, , vol. 11(2), pages 95-101, June.
    • Handle: RePEc:sae:medema:v:11:y:1991:i:2:p:95-101
    DOI: 10.1177/0272989X9101100204
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    Cited by:

    1. J Nevil Amos & Andrew F Bennett & Ralph Mac Nally & Graeme Newell & Alexandra Pavlova & James Q Radford & James R Thomson & Matt White & Paul Sunnucks, 2012. "Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-12, February.
    2. Pahalage Dhanushka Sandaruwan & Champi Thusangi Wannige, 2021. "An improved deep learning model for hierarchical classification of protein families," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-15, October.
    3. Arthur De Sá Ferreira & Ney Meziat-Filho & Ana Paula Antunes Ferreira, 2021. "Double threshold receiver operating characteristic plot for three-modal continuous predictors," Computational Statistics, Springer, vol. 36(3), pages 2231-2245, September.
    4. V. Robles & C. Bielza & P. Larrañaga & S. González & L. Ohno-Machado, 2008. "Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 345-366, December.
    5. Bernd Lütkenhöner & Türker Basel, 2013. "Predictive Modeling for Diagnostic Tests with High Specificity, but Low Sensitivity: A Study of the Glycerol Test in Patients with Suspected Menière’s Disease," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    6. Wilbert B. van den Hout, 2003. "The Area under an ROC Curve with Limited Information," Medical Decision Making, , vol. 23(2), pages 160-166, March.
    7. D. J. Hand & C. Anagnostopoulos, 2023. "Notes on the H-measure of classifier performance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 109-124, March.

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