The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show intersections is proposed. In particular, the relationship between ROC orderings and stochastic dominance is investigated in a theoretical framework and ...

The area under the ROC curve (AUC) is a well-known measure of ranking performance, and is also often used as a measure of classification performance, aggregating over decision thresholds as well as class and cost skews. However, David Hand has recently argued that AUC is fundamentally incoherent as a measure of aggregated classifier performance and proposed an alternative measure [5]. Specifica...

This article considers receiver operating characteristic (ROC) analysis for bivariate marker measurements. The research interest is to extend tools and rules from univariate marker to bivariate marker setting for evaluating predictive accuracy of markers using a tree-based classification rule. Using an and-or classifier, an ROC function together with a weighted ROC function (WROC) and their con...

The biometric matching problem is a two class (“within” or “between”) classification problem where two types of errors (FRR and FAR) occur. While the receiver operating characteristic or ROC curve, which is a plot of FRR and FAR, can be easily obtained in the simple matching model, it is non-trivial to obtain in the multivariate matching model. Here the problem of obtaining ROC curves for sever...

Compare the accuracy of two continuous-scale tests is increasing important when a new test is developed. The traditional approach that compares the entire areas under two Receiver Operating Characteristic (ROC) curves is not sensitive when two ROC curves cross each other. A better approach to compare the accuracy of two diagnostic tests is to compare the areas under two ROC curves (AUCs) in the...

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 sciences. ROC curves are frequently summarized in a single scalar, the area under the curve (AUC). This article discusses the caveats and pitfalls of ROC analysis in clinical microarray research, particularly in...

The Receiver Operating Characteristic (ROC) curve is a two dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this note, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts. This allows for an interpretati...

Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the area under the receiver operating characteristic (ROC) curve, AUC, is a useful onenumber summary index for the diagnostic accuracy of the test. When only a particular region of the ROC curve would be ...

BACKGROUND Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. METHODS We recall the foundations of...

Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural ne...