OBJECTIVE To validate the SICK scoring system's ability to differentiate between individuals with higher and lower probabilities of death METHOD We performed a one year two-centre prospective evaluation of all children aged between one month and 12 years referred to the Paediatric team at St Stephens Hospital in Delhi and admitted to the Paediatric Department at West Middlesex University Hosp...

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...

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...

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...

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of FPFs ...