Results 21 to 30 of about 127,975,471 (298)
Nonparametric confidence intervals for the area under the ROC curve
Statistica, 2013 Following an idea by Jing et al. (2005), this paper combines the empirical likelihood for the mean functional with jackknife pseudo-values obtained from the Mann-Witney two-sample statistic.
Gianfranco Adimari
doaj +1 more source
Analysis of area under the ROC curve of energy detection [PDF]
IEEE Transactions on Wireless Communications, 2010 A simple figure of merit to describe the performance of an energy detector is desirable. The area under the receiver operating characteristic (ROC) curve, denoted (AUC), is such a measure, which varies between 1/2 and 1. If the detector's performance is no better than flipping a coin, then the AUC is 1/2 , and it increases to one as the detector ...
Saman Atapattu +2 more
openaire +1 more source
IEEE Access, 2020 Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking “negative samples” (i.e.
Junlin Xu +8 more
doaj +1 more source
Receiver Operator Characteristic Analysis of Biomarkers Evaluation in Diagnostic Research [PDF]
Journal of Clinical and Diagnostic Research, 2018 Receiver Operator Characteristic (ROC) analysis is the choice of method in evaluation of biomarkers in bioinformatics research. However, there is no single method and also no single accuracy index in evaluating diagnostic tools.
Karimollah Hajian-Tilaki
doaj +1 more source
Empirical Likelihood Inference for the Area under the ROC Curve
Biometrics, 2005 Summary For a continuous‐scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the
Qin, Gengsheng, Zhou, Xiao-Hua
openaire +4 more sources
Inference about time-dependent prognostic accuracy measures in the presence of competing risks
BMC Medical Research Methodology, 2020 Background Evaluating a candidate marker or developing a model for predicting risk of future conditions is one of the major goals in medicine. However, model development and assessment for a time-to-event outcome may be complicated in the presence of ...
Rajib Dey +2 more
doaj +1 more source
Score Fusion by Maximizing the Area under the ROC Curve [PDF]
, 2009 Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve.
Mauricio Villegas, Roberto Paredes
openaire +3 more sources
Soft Attention Based DenseNet Model for Parkinson’s Disease Classification Using SPECT Images
Frontiers in Aging Neuroscience, 2022 ObjectiveDeep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting the condition is
Mahima Thakur +4 more
doaj +1 more source
Incremental Area Under the Curve
, 2022 RStudio script to calculate various types of areas under the curve as outlined in Brouns, F., Bjorck, I., Frayn, K.N., Gibbs, A.L., Lang, V., Slama, G. and Wolever, T.M.S.
Lacey, Seán
core +1 more source
Reasonably conduct the multiple Logistic regression analysis combined with the ROC curve analysis
Sichuan jingshen weisheng, 2022 The purpose of this paper was to introduce how to reasonably carry out the method of the multiple Logistic regression analysis by combining the ROC curve analysis. Firstly， it introduced two groups of the basic concepts related to the ROC curve analysis，
Hu Chunyan, Hu Liangping
doaj +1 more source