Abstract
Receiver Operating Characteristic (ROC) analysis is a methodology that has gained much popularity in our days, especially in Medicine, since through the ROC curves, it provides a useful tool to evaluate and specify problems in the performance of a diagnostic indicator.
The area under empirical ROC curve (AUC) it’s an indicator that can be used to compare two or more ROC curves.
This work arose from the necessity of the existence of software that allows the calculation of the necessary measures to compare systems based on ROC curves.
Several software, commercial and non-commercial, are available to perform the calculation of the measures associated to the ROC analysis. However, they present some flaws, especially when there is a need to compare independent samples with different dimensions, or also to compare two ROC curves that intersect.
In this paper is presented a new application called CERCUS (Comparison of Empirical ROC Curves). This was developed using a programming language (Java) and stands out for the possibility of comparing two or more ROC curves that cross each other.
The main objective of CERCUS is the calculation of several ROC estimates using different methods and make the ROC curves comparison, even if there is an intersection, either for independent or paired samples. It also allows the graph representation of the ROC curve in a unitary plan as well the graph of the area between curves in comparison.
This paper presents the program’s versatility in data entry, test menus and visualization of graphs and results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Braga, A.C., Costa, L., Oliveira, P.: An alternative method for global and partial comparison of two diagnostic systems based on ROC curves. J. Stat. Comput. Simul. 83(2), 307–325 (2013)
Braga, A.C., Frade, H., Carvalho, S., Santiago, A.M.: Package ‘Comp2ROC’ (2014). https://cran.r-project.org/web/packages/Comp2ROC/Comp2ROC.pdf
Braga, A.C., Oliveira, P.: Diagnostic analysis based on ROC curves: theory and applications in medicine. Int. J. Health Care Qual. Assur. 16(4), 191–198 (2003)
Cheam, A., McNicholas, P.D.: Modelling receiver operating characteristic curves using gaussian mixtures, pp. 1–15 (2014)
Delong, E.R., Delong, D.M., Clarke-pearson, D.L., Carolina, N.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3), 837–845 (1988)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Frade, H., Braga, A.C.: Comp2roc. In: Mohamad, M.S., Nanni, L., Rocha, M.P., Fdez-Riverola, F. (eds.) 7th International Conference on Practical Applications of Computational Biology & Bioinformatics. AISC, vol. 222, pp. 127–135. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00578-2_17
Greenberg, I., Xu, D., Kumar, D.: Processing Creative Coding and Generative Art in Processing 2. Apress, Berkeley (2013). https://doi.org/10.1007/978-1-4302-4465-3
Hajian-Tilaki, K.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation (2013)
Hanley, A., McNeil, J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982)
Hanley, J.A., McNeil, B.J.: A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148(3), 839–843 (1983)
Mourão, M.F., Braga, A.C.: Evaluation of the CRIB as an indicator of the performance of neonatal intensive care units using the software ROCNPA. In: 2012 12th International Conference on Computational Science and Its Applications, pp. 151–154, June 2012. https://doi.org/10.1109/ICCSA.2012.37
Acknowledgments
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Moreira, D., Braga, A.C. (2019). Comparing Empirical ROC Curves Using a Java Application: CERCUS. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-24302-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24301-2
Online ISBN: 978-3-030-24302-9
eBook Packages: Computer ScienceComputer Science (R0)