ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this note we present some new findings and connections between ROC space and cost space, by using the expected lo...

A novel method for receiver operating characteristic (ROC) curve analysis and anomaly detection is proposed. The ROC curve provides a measure of effectiveness for binary classification problems, and this paper specifically addresses unbalanced, unsupervised, binary classification problems. Furthermore, this work explores techniques in fusing decision values from classifiers and using ROC curves...

The Receiver Operating Characteristic (ROC) curve generated based on assuming a constant shape Bi-Weibull distribution is studied. In the context of ROC curve analysis, it is assumed that biomarker values from controls and cases follow some specific distribution and the accuracy is evaluated by using the ROC model developed from that specified distribution. This article assumes that the biomark...

This article proposes a new non-parametric approach for identification of risk factors and their correlations in epidemiologic study, in which investigation data may have high variations because of individual differences or correlated risk factors. First, based on classification information of high or low disease incidence, we estimate Receptor Operating Characteristic (ROC) curve of each risk ...

An overview of the usage of Receiver Operating Characteristic (ROC) analysis within medicine and computer science is given. A survey of the theory behind the analysis is given together with a presentation of how to create the ROC curve and different methods to interpret it. Both methods that rely on binormal distributions and methods that rely on distribution free methods have been mentioned. A...

The ROC curve is one of the most widely used visual tool to evaluate performance of scoring functions regarding their capacities to discriminate between two populations. It is the goal of this paper to propose a statistical learning method for constructing a scoring function with nearly optimal ROC curve. In this bipartite setup, the target is known to be the regression function up to an increa...

Background: Among different roles of miRNAs in AD pathogenesis, hsa-miR-494-3p and hsa-miR-661 functions are poorly understood. Methods: To obtain the gene targets, gene networks, gene ontology, and enrichment analysis of the two miRNAs, some web servers were utilized. Furthermore, the expressions of these miRNAs were analyzed by qRT-PCR in 36 blood sera, including 18 Alzheimer’s patients and 1...

The limitations of diagnostic "accuracy" as a measure of decision performance require introduction of the concepts of the "sensitivity" and "specificity" of a diagnostic test. These measures and the related indices, "true positive fraction" and "false positive fraction," are more meaningful than "accuracy," yet do not provide a unique description of diagnostic performance because they depend on...

Abstract The receiver operating characteristic (ROC) curve is defined as true positive rate versus false positive rate obtained by varying a decision threshold criterion. It has been widely used in medical science for its ability to measure the accuracy of diagnostic or prognostic tests. Mathematically speaking, ROC curve is the composition of survival function of one population with the quanti...