Measures including sensitivity, specificity, and positive and negative predictive values have been traditionally used to assess a diagnostic test's ability to detect the presence or absence of disease. Receiver operating characteristic (ROC) curve analysis allows visual evaluation of the trade-offs between sensitivity and specificity associated with different values of the test result, or diffe...

ROC curve analysis is used widely in medicine as a method for evaluating the performance of diagnostic tests (3,5,6,10), but has been used recently in many agricultural applications (2,4,5,11,12). The ROC curve provides information regarding how often a test’s predictions are correct, and provides a graphical method for evaluating and discriminating between different diagnostic tests or modific...

1. Introduction Many diagnostic tests are quantitative especially in clinical chemistry. Receiver operating characteristic (ROC) analysis is well recognized statistical tool for the evaluation how well the diagnostic test can discriminate diseased and non-diseased populations. The area under ROC curve (AUC) is often used to summarize the diagnostic accuracy of the diagnostic test. This area, a ...

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...

This paper demonstrates that, for large-scale tests, the match and non-match similarity scores have no specific underlying distribution function. The forms of these distribution functions require a nonparametric approach for the analysis of the fingerprint similarity scores. In this paper, we present an analysis of the discrete distribution functions of the match and non-match similarity scores...

In this note we extend the well-known binormal model via implementation of the epsilon-skew-normal (ESN) distribution developed by Mudholkar and Hutson (2000). We derive the equation for the receiver operating characteristic (ROC) curve assuming epsilon-skew-binormal (ESBN) model and examine the behavior of the maximum likelihood estimates for estimating the ESBN parameters. We then summarize t...

Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories)...

The role of background synaptic activity in cortical processing has recently received much attention. How do individual neurons extract information when embedded in a noisy background? When examining the impact of a synaptic input on postsynaptic firing, it is important to distinguish a change in overall firing probability from a true change in neuronal sensitivity to a particular input (synapt...

Receiver operating characteristic (ROC) curves are frequently used to compare the accuracy of two or more imaging modalities. This paper addresses the use of ROC analysis to evaluate the speed and accuracy of digital mammography, as compared to conventional film-screen mammography.