Performance evaluation of any classification method is fundamental to its acceptance in practice. Evaluation should consider the dependence of a classifier’s accuracy on relevant covariates in addition to its overall accuracy. When developing a classifier with a continuous output that allocates units into one of two groups, receiver operating characteristic (ROC) curve analysis is appropriate. ...

Influence of Imprecision on ROC Curve Analysis for Cardiac Markers, Peter Kupchak, Alan H.B. Wu, Farooq Ghani, L. Kristen Newby, E. Magnus Ohman, and Robert H. Christenson ( Nanogen, Point of Care Diagnostics Division, Toronto, Ontario, Canada; 2 Department of Laboratory Medicine, San Francisco General Hospital/University of California, San Francisco, CA; 3 Bayer Healthcare Corporation, Tarryto...

Interleukin-6 is a biomarker of inflammation which has been suggested as having potential discriminatory ability for myocardial infarction. Because of its high assaying cost it is very expensive to evaluate this marker. In order to reduce this cost we propose pooling the specimens. In this paper we examine the efficiency of ROC curve analysis, specifically the estimation of the area under the R...

background identification of a normal range for biomarkers, based on pregnancy outcomes (caused by their high or low values) is of special importance in clinical studies. as some pregnancy outcomes can happen in both high and low levels of biomarkers, the receiver-operating characteristic (roc) curve is unsuitable for identifying these levels separately; rather, a statistical method is preferab...

This paper describes how to use Receiver Operator Characteristic (ROC) analysis to evaluate radiologists’ performance of interpreting digital mammograms in real-time. We developed an experimental testing system, which implemented a set of clinical lesion-matching rules to prepare raw ROC data. The system can automatically provide detailed evaluations of the performance, such as sensitivity, spe...

The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under...

The Receiver Operating Characteristic (ROC) Curve originated during Second World War to analyse radar signals by observing the relationship between signal and noise [1]. The framework of ROC Curve has a wide spread of interdisciplinary applications such as human perception, decision making [2], industrial quality control [3], military monitoring [4] etc. In later years, the applications of ROC ...