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In medicine, ROC analysis has been extensively used in the evaluation of diagnostic tests. [57] [58] ROC curves are also used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidence-based medicine. In radiology, ROC analysis is a common technique to evaluate new radiology techniques. [59]
ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on the x axis. An image of different ROC curves is shown in Figure 1. ROC curves provide a simple visual method for one to determine the boundary limit (or the separation threshold) of a biomarker or a combination of biomarkers for ...
Youden's index is often used in conjunction with receiver operating characteristic (ROC) analysis. [4] The index is defined for all points of an ROC curve, and the maximum value of the index may be used as a criterion for selecting the optimum cut-off point when a diagnostic test gives a numeric rather than a dichotomous result.
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
The receiver operating characteristic (ROC) also characterizes diagnostic ability, although ROC reveals less information than the TOC. For each threshold, ROC reveals two ratios, hits/(hits + misses) and false alarms/(false alarms + correct rejections), while TOC shows the total information in the contingency table for each threshold. [2]
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. An example of ROC curve and the area under the curve (AUC). The area under the ROC curve (AUC) [1] [2] is often used to summarize in a single number the diagnostic ability of the classifier. The AUC is simply ...
A randomized controlled trial compared how well physicians interpreted diagnostic tests that were presented as either sensitivity and specificity, a likelihood ratio, or an inexact graphic of the likelihood ratio, found no difference between the three modes in interpretation of test results. [10]
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).