Add to ORKGResampling procedures allows a better use of ROC curves and AUC for predictive purposes. We also address a drawback of AUC for the comparison of ROC curves which are crossing, by recommending the use of partial AUC
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>AUC (95% C.I.) of the ROC curve analysis to discriminate patients and controls, patients with DR ...
<p>AUC of ROC curve for the prediction of mortality at the time of CRRT application.</p
<p>Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p
The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessin...
<p>A) Traditional ROC curve. The horizontal dash line indicates the region of interest for the parti...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...
Our new proposed criteria were significantly superior to the old criteria (P < 0.001). Note. AUC; ar...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>AUC (95% C.I.) of the ROC curve analysis to discriminate patients and controls, patients with DR ...
<p>AUC of ROC curve for the prediction of mortality at the time of CRRT application.</p
<p>Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p
The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessin...
<p>A) Traditional ROC curve. The horizontal dash line indicates the region of interest for the parti...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
<p>The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...
<p>ROC curves are plots of sensitivity and specificity of algorithms for distinguishing normal contr...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...
Our new proposed criteria were significantly superior to the old criteria (P < 0.001). Note. AUC; ar...
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>AUC (95% C.I.) of the ROC curve analysis to discriminate patients and controls, patients with DR ...
<p>AUC of ROC curve for the prediction of mortality at the time of CRRT application.</p