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. 2017:2017:3762651.
doi: 10.1155/2017/3762651. Epub 2017 May 31.

Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

Ilker Unal  1
Affiliations

Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

Ilker Unal. Comput Math Methods Med. 2017.

Abstract

ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point.

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Figures

Figure 1
Figure 1
The receiver operator characteristic curve for pulse pressure in the prediction of cardiovascular death [12].
Figure 2
Figure 2
The empirically estimated objective functions IU(c) under different underlying distributions: light to dark colors represent the scenarios with the classification accuracies from poor to high one. The homoscedastic gamma distribution scenario with a balanced design (n0 = n1 = 100) is represented.
Figure 3
Figure 3
The receiver operator characteristic curves for LVEF, plasma sodium, and heart rate in the prediction of cardiovascular death [12].
See this image and copyright information in PMC

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