Receiver operating characteristic (ROC) analysis is a tool used to describe the
discrimination accuracy of a diagnostic test or prediction model. While sensitivity and …

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

Praise for the First Edition"... the book is a valuable addition to the literature in the field,
serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt …

Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing
classifiers and visualizing their performance. ROC graphs are commonly used in medical …

This paper gives an overview of some ways in which our understanding of performance
evaluation measures for machine-learned classifiers has improved over the last twenty …

We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial
neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of …

Receiver operating characteristic (ROC) curves have been useful in two‐group classification
problems. In three‐and multiple‐class diagnostic problems, an ROC surface or hyper …

The accuracy of a single diagnostic test for binary outcome can be summarized by the area
under the receiver operating characteristic (ROC) curve. Volume under the surface and …

Nowadays the area under the receiver operating characteristics (ROC) curve, which
corresponds to the Wilcoxon–Mann–Whitney test statistic, is increasingly used as a …

RATIONALE AND OBJECTIVES: Investigators often need to assess the accuracies of
diagnostic tests when the gold standard is not binary-scale. The objective of this article is to …