ROC Curve, AUC & Diagnostic Performance in ExcelDiagnostic performance evaluates the ability of a qualitative or quantitative test to discriminate between two subclasses of subjects.

Measures of diagnostic accuracy
Diagnostic accuracy measures the ability of a test to detect a condition when it is present and detect the absence of a condition when it is absent.
ROC plot
ROC (receiver operating characteristic) curves show the ability of a quantitative diagnostic test to classify subjects correctly as the decision threshold is varied.
Area under the curve (AUC)
The area under (a ROC) curve is a measure of the accuracy of a quantitative diagnostic test.
Difference between the areas under two curves
The difference between areas under the ROC curves compares the accuracy of two or more diagnostic tests.
Decision thresholds
A decision threshold is a value that dichotomizes the result of a quantitative test to a simple binary decision.
Decision plot
A decision plot shows a measure of performance (such as sensitivity, specificity, likelihood ratios, or predictive values) against all decision thresholds to help identify optimal decision threshold.

Related concepts

ROC curves and AUC explained

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From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/diagnostic-performance