The ROC curves are used in the evaluation of the clinical accuracy of a diagnostic test applied to diseased and nondiseased populations. An ROC curve is a plot of the sensitivity of a test versus 1-specificity. Sensitivity is the fraction of the diseased population with a positive test while specificity is the fraction of the nondiseased population with a negative test. Therefore, the ROC plots the true positive fraction versus the false positive fraction. Furthermore, the area under an ROC curve is used as an index of the diagnostic accuracy of the respective test. The area under the curve decreases as the uncertainty of measurement of the diagnostic test increases [1].

Assuming

is the area under an ROC curve and

is the area over the ROC curve for an uncertainty of measurement

, the changes

and

are defined as

and

, the relative changes as

and

, the rates of change as

and

, and the relative rates of change as

and

, respectively.

As

, it can be considered that the area over the ROC curve is an index of diagnostic inaccuracy. In fact, as the plots of this Demonstration show, the relative change, the rate of change and the relative rate of change of the area over an ROC curve versus the uncertainty of measurement are greater than the absolute value of the respective measures of the area under the ROC curve for the same populations.

To the best of the author's knowledge, measures of the area over the ROC curve versus the uncertainty of measurement have not been discussed in the literature except for [2].

The area over the ROC curve could be used in the evaluation of a diagnostic test as a diagnostic inaccuracy index. For example, in the thumbnail and the snapshots, the population data describes a bimodal distribution of serum glucose on a nondiabetic and a diabetic population [3].

[3] T.-O. Lim, R. Bakri, Z. Morad and M. A. Hamid, "Bimodality in Blood Glucose Distribution—Is It Universal?,"

*Diabetes Care,* **25**(12), 2002 pp. 2212–2217.

doi:10.2337/diacare.25.12.2212.