Statistical evaluation of diagnostic performance in general and Receiver Operating
Characteristic (ROC) analysis in particular are important for assessing the performance of …

With the growing focus on comparative effectiveness research and personalized medicine,
receiver-operating characteristic analysis can continue to play an important role in health …

Receiver operating characteristic (ROC) curves are frequently used in biomedical
informatics research to evaluate classification and prediction models for decision support …

Receiver operating characteristic (ROC) analysis, which yields indices of accuracy such as
the area under the curve (AUC), is increasingly being used to evaluate the performances of …

Diagnostic medicine has progressed tremendously in the last several decades, and the
trend promises to continue well into the next millennium. Advances in technology provide …

Assessment of predictive accuracy is a critical aspect of evaluating and comparing models,
algorithms or technologies that produce the predictions. In the field of medical diagnosis …

The receiver operating characteristic (ROC) has emerged as the gold standard for assessing
and comparing the performance of classifiers in a wide range of disciplines including the life …

A. What Is ROC Analysis? Receiver operating characteristic (ROC) analysis is a procedure,
derived from statistical decision theory, that was developed in the context of electronic signal …

RATIONALE AND OBJECTIVES: Several authors have encouraged the use of a quasi-
continuous rating scale for data collection in receiver operating characteristic (ROC) curve …

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate
classifiers in biomedical and bioinformatics applications. However, conclusions are often …