The AUC results for receiver operator characteristic (ROC) curves for the prediction of whether or not a cluster would acquire new cases based on recency (collection date) plotted against the distance threshold used to define clustering. The same result was also calculated without a bootstrap requirement for clustering (dashed) and with diagnostic dates used to measure recency (green). The optimal threshold determined in Fig 4 for each data set is marked in red. (TIF)</p
<p>The ROC curve is plotted with the <i>Sn</i> as the <i>y</i>-axis and 1 − <i>Sp</i> as the <i>x</i...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>Area Under the Curve (AUC) was obtained from the ROC curves of 9 predictors: AUC cannot be comput...
<p>Area under the ROC curve (AUC) for predictive indices in the derivation and valdation data sets.<...
<p>The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...
Abstract: In the literature, there are several criteria for validation of a clustering partition. Th...
ROC curve of median case in single-image-unit-based-prediction (a) and patient-unit-based-prediction...
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
ROC curve was calculated when using source activity and surface potentials, for each case we use the...
Difference in AIC (ΔAIC) between Poisson-linked models of cluster growth for four separate data sets...
<p>Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p
The AIC difference between two Poisson-linked models of cluster growth for all four full data sets, ...
<p>The ROC curve is plotted with the <i>Sn</i> as the <i>y</i>-axis and 1 − <i>Sp</i> as the <i>x</i...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>Area Under the Curve (AUC) was obtained from the ROC curves of 9 predictors: AUC cannot be comput...
<p>Area under the ROC curve (AUC) for predictive indices in the derivation and valdation data sets.<...
<p>The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...
Abstract: In the literature, there are several criteria for validation of a clustering partition. Th...
ROC curve of median case in single-image-unit-based-prediction (a) and patient-unit-based-prediction...
<p>The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...
ROC curve was calculated when using source activity and surface potentials, for each case we use the...
Difference in AIC (ΔAIC) between Poisson-linked models of cluster growth for four separate data sets...
<p>Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p
The AIC difference between two Poisson-linked models of cluster growth for all four full data sets, ...
<p>The ROC curve is plotted with the <i>Sn</i> as the <i>y</i>-axis and 1 − <i>Sp</i> as the <i>x</i...
<p> <b>The AUC (ROC score) is the area under the ROC curve, normalized to 100 for a ...
We compute the area under the curve (AUC) for each model and each cohort, where a perfect classifier...