Sample size tables for receiver operating characteristic studies.

Using Chebyshev’s Inequality to Determine Sample Size in Biometric Evaluation of Fingerprint Data

The fingerprint datasets in some cases may exceed a million of samples. The underlying distribution functions of the similarity scores are unknown. Therefore, the needed size of a biometric evaluation test set is an important question in terms of both efficiency and accuracy. In this article, Chebyshev’s inequality, in combination with simple random sampling, is used to determine the sample siz...

Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation

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...

DETECTING OUTLIERS AND INFLUENTIAL DATA POINTS IN RECEIVER OPERATING CHARACTERISTIC (ROC) ANALYSIS by

Receiver operating characteristic (ROC) curve for medical researchers.

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)...

Using Receiver Operating Characteristic (ROC) Curves to Evaluate Digital Mammography

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.