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Ordered multiple-class ROC analysis with continuous measurements.

Christos T Nakas1, Constantin T Yiannoutsos

  • 1Division of Biostatistics, Indiana University School of Medicine, 1050 Wishard Boulevard, RG 4101, Indianapolis, IN 46202-2872, USA.

Statistics in Medicine
|October 27, 2004
PubMed
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This study introduces ROC surfaces for multi-class diagnostic problems, extending ROC curves. The volume under these surfaces offers a new method for analyzing classification performance with ordered categories.

Area of Science:

  • Statistics
  • Medical Diagnostics
  • Machine Learning

Background:

  • Receiver operating characteristic (ROC) curves are standard for binary classification.
  • Extending ROC analysis to multi-class problems presents challenges in visualization and interpretation.

Purpose of the Study:

  • To define and investigate ROC surfaces and hyper-surfaces for multi-class diagnostic problems.
  • To simplify ROC surface definitions for ordered categories in three-class and multi-class scenarios.
  • To explore the utility of ROC surfaces for continuous and ordinal data.

Main Methods:

  • Construction of ROC surfaces and hyper-surfaces for multi-group classification.
  • Utilizing bootstrap techniques and U-statistics theory for inference.
  • Non-parametric manipulation of continuous and discrete test data.

Related Experiment Videos

  • Comparison of diagnostic tests on the same subjects.

    • Main Results:

    • ROC surfaces provide a less complex definition for ordered categories in multi-class problems.
    • The volume under ROC surfaces can be used for statistical inference.
    • Demonstrated utility in a three-group classification example for HIV neurological disease.

    Conclusions:

    • ROC surfaces offer a valuable extension of ROC analysis for multi-class diagnostic problems.
    • The proposed method simplifies analysis for ordered categories and various data types.
    • This approach enhances the evaluation of diagnostic tests in complex classification tasks.