The analysis of receiver operating characteristic curve and comparison of the areas under the curve

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The evaluative operations used to make a decision depending on laboratory techniques, clinical observations or various measurements to determine if individuals are healthy or not are called diagnostic tests. A positive or negative diagnosis is made by comparing the measurement with a cutoff value. A receiver operating characteristic (ROC) curve shows the characteristics of a diagnostic test by graphing the false-positive rate (1-specificity) on the horizontal axis and the true-positive rate (sensitivity) on the vertical axis for various cutoff values. The area under of a ROC curve (AUC) is a popular measure for the accuracy of a diagnostic test. A diagnostic test which has a larger AUC makes a better predict for the existence of a disease. Two methods are used to widely estimate the AUC. These include the binormal and the empirical (nonparametric) methods. The purpose of this paper is to give a reviewed information about the research of ROC curves, AUC, estimation methods and to compare AUC of two diagnostic tests using a hypothesis test.

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ROC Curve AUC Binormal Nonparametric Sensitivity Specificity

Journal Of Experimental an‎d Clinical Medicine

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Journal Of Experimental an‎d Clinical Medicine