The easiest way to perform ROC analysis!
A receiver operating characteristics (ROC) curve is a graphical approach which assess the performance of a binary classifier system. The ROC curve analysis is widely used in medicine, radiology, biometrics and various application of machine learning.
Here we developed an easy way to carry out ROC analysis. This application creates ROC curves, calculates area under the curve (AUC) values and confidence intervals for the AUC values, and performs multiple comparisons for ROC curves in a user-friendly, up-to-date and comprehensive way. Moreover, easyROC computes and compares partial AUCs. It can also perform sample size calculation.
An important feature of this application is to determine cut-off values especially for diagnostic tests. For this task, we made use of OptimalCutpoints package (Lopez-Raton et al, 2014) of R.
This tool freely available through http://www.biosoft.hacettepe.edu.tr/easyROC/.