Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach
By Ilker Ünal. Comput. Math. Methods Medicine, 2017
728 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Health Information Management, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/72178c2a8d7c42b2ddbc8f10b167ff6077a80895
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Health Information Management
In Statistics and Probability
Citations in the corpus
- Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, methodology): “, Youden index (J) method, concordance probability method) (Unal, 2017) and that the”
- Receiver operating characteristic curve: overview and practical use for clinicians (2022, methodology): “Index of union (IU) IU uses the absolute difference between the diagnostic measurement and the AUC value to minimize the misclassification rate, calculated using the following formula [28]: (5) IU = (|Se − AUC| + |Sp − AUC|)”