<p>ROC of our new model, PSA, PSAD and f/t. The AUC of these predictors were 0.789, 0.566, 0.664 and 0.654 respectively.</p
Receiver operating characteristic (ROC) analysis is used for comparing predictive models, both in mo...
<p>The ROC AUC was 0.7686. The straight line represented the ROC curve expected by chance alone.</p
§<p>PPV: positive predictive value;</p>¢<p>NPV: negative predictive value;</p>£<p>AUC: area under th...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
ROC curves for prediction of KD with CALs by the new scoring model, AUC: 0.838(95% CI, 0.781–0.895)....
<p>The red, solid lines show the AUC's for the models after addition of MRI reading. The model for r...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
<p>Model comparison in terms of AU-ROC differences and confidence intervals for the different progno...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
ROC curves for the top performing model compared to individual feature predictions.</p
<p>Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p
<p>FPR represents the false positive rate, and TPR is the true positive rate. The ROC curve is close...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
Model A (blue line) incorporates classical risk factors and five GWAS-identified SNPs, whereas model...
<p>The AUC is the integral of the area below the ROC curve, as shown in <a href="http://www.plosone....
Receiver operating characteristic (ROC) analysis is used for comparing predictive models, both in mo...
<p>The ROC AUC was 0.7686. The straight line represented the ROC curve expected by chance alone.</p
§<p>PPV: positive predictive value;</p>¢<p>NPV: negative predictive value;</p>£<p>AUC: area under th...
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
ROC curves for prediction of KD with CALs by the new scoring model, AUC: 0.838(95% CI, 0.781–0.895)....
<p>The red, solid lines show the AUC's for the models after addition of MRI reading. The model for r...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
<p>Model comparison in terms of AU-ROC differences and confidence intervals for the different progno...
(A) Performance of the model in the training set, which showed an AUC value of 0.768, an optimal cut...
ROC curves for the top performing model compared to individual feature predictions.</p
<p>Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p
<p>FPR represents the false positive rate, and TPR is the true positive rate. The ROC curve is close...
<p>(A) ROC curve of the 349-gene predictive model in training set (200 samples, AUC = 0.826; <i>p<</...
Model A (blue line) incorporates classical risk factors and five GWAS-identified SNPs, whereas model...
<p>The AUC is the integral of the area below the ROC curve, as shown in <a href="http://www.plosone....
Receiver operating characteristic (ROC) analysis is used for comparing predictive models, both in mo...
<p>The ROC AUC was 0.7686. The straight line represented the ROC curve expected by chance alone.</p
§<p>PPV: positive predictive value;</p>¢<p>NPV: negative predictive value;</p>£<p>AUC: area under th...
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