In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate against the false positive rate at various threshold settings. (The true-positive rate is also known as sensitivity in biomedical informatics, ...

At any point on a receiver operating characteristic (ROC) curve in combination with two distributions of genuine scores and impostor scores, there are three related variables: the true accept rate (TAR) of the genuine scores, the false accept rate (FAR) of the impostor scores, and the threshold. Any one of these three variables determines the other two variables. The measures and accuracies of ...

• This article reviews current state of the art of ROC surface analysis and illustrates its use through an application on a pancreatic cancer diagnostic marker. Receiver Operating Characteristic (ROC) surfaces have been studied in the literature essentially only during the last decade and are considered as a natural generalization of ROC curves in three-class diagnostic problems. This article p...

• The accuracy of a binary diagnostic test can easily be assessed by comparing the sensitivity and specificity with the status of respondents. When the result of a diagnostic test is continuous, the assessment of accuracy depends on a specified threshold. The receiver operating characteristic (ROC) curve, which includes all possible combinations of sensitivity and specificity, provides an appro...

The receiver operating characteristic (ROC) curve is one of the most commonly used methods to compare the diagnostic performances of two or more laboratory or diagnostic tests. In this thesis, we propose semi-empirical likelihood based confidence intervals for ROC curves of two populations, where one population is parametric while the other one is non-parametric and both populations have missin...

Developing novel nuclear cardiology approaches requires evaluating their diagnostic ability. Statistical measures such as sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) are useful to evaluate the diagnostic value of novel imaging parameters. This paper reviews key statistical methods used in the evaluation of diagnostic tests and highligh...

The receiver operating characteristic curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests involves multiple readers and multiple tests, in which all readers read all test results from the same patients. This design is most commonly used in a radiology setting, where th...

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly ch...

This review provides the basic principle and rational for ROC analysis of rating and continuous diagnostic test results versus a gold standard. Derived indexes of accuracy, in particular area under the curve (AUC) has a meaningful interpretation for disease classification from healthy subjects. The methods of estimate of AUC and its testing in single diagnostic test and also comparative studies...