The purpose of this tutorial-based lecture is to show the usefulness of performing a receiver operating characteristic (ROC) curve analysis. The lecture will explain the background terminology associated with a ROC curve analysis, show the necessary SAS software coding to run a ROC curve analysis, and finally help interpret the analysis output in order to make informed research decisions. An ex...

Tremendous success of machine learning algorithms at pattern recognition creates interest in new inventions. Machine an era big data is that significant hierarchical relationships within the can be discovered algorithmically than other handcraft like features. In this study, Convolutional Neural Network (CNN) used as feature descriptors pulmonary malignancy prediction. Various such Histogram Or...

Received Jun 5, 2016 Revised Aug 8, 2016 Accepted August 24, 2016 Receiver operating characteristic (ROC) curve is an important technique for organizing classifiers and visualizing their performance in tactical systems in the presence of jamming signal. ROC curves are commonly used to evaluate the performance of classifiers for anomalies detection. This paper gives a survey of ROC analysis base...

The receiver operating characteristic (ROC) curve, which is defined as a plot of test sensitivity as they coordinate versus its 1-specificity or false positive rate (FPR) as the x coordinate, is an effective method of evaluating the performance of diagnostic tests. The purpose of this article is to provide a nonmathematical introduction to ROC analysis. Important concepts involved in the correc...