<p>Sensitivity (Se%), Specificity (Sp%) and area under the ROC curve (ROC-AUC) for the classification of SB, LPA and MVPA using EE combined with direct observation as the criterion measure.</p
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>Receiver operating characteristic (ROC) curves and corresponding areas under the curve (AUCs) com...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).</p
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
<p>ROC curves for determining the sensitivity and specificity of the overall assessment of the PHD-P...
Areas under ROC curve (AUC) and sensitivities (%) at fixed specificities of 80% and 90% obtained wit...
<p>The curve presents the true positive rate (or sensitivity) in function of false positive rate for...
<p>NB: AUC–Area under curve denotes the area in the graph below Receiver Operating Characteristic (R...
The sensitivity, specificity, positive likelihood ratio, correct classification, and area under rece...
In this case, we obtain the sensitivity = 80 % (80/100), specificity = 90 % (90/100), PPV = 88.9 % (...
<p>The values indicated are weighted averages for the three classes under consideration; control, MC...
<p>ROC curves analyses to represent sensitivity/specificity of each biomarker, and the Area Under th...
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>Receiver operating characteristic (ROC) curves and corresponding areas under the curve (AUCs) com...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Sensitivity and specificity values were obtained for increasing classification thresholds to prod...
<p>ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).</p
<p>This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...
<p>ROC curves for determining the sensitivity and specificity of the overall assessment of the PHD-P...
Areas under ROC curve (AUC) and sensitivities (%) at fixed specificities of 80% and 90% obtained wit...
<p>The curve presents the true positive rate (or sensitivity) in function of false positive rate for...
<p>NB: AUC–Area under curve denotes the area in the graph below Receiver Operating Characteristic (R...
The sensitivity, specificity, positive likelihood ratio, correct classification, and area under rece...
In this case, we obtain the sensitivity = 80 % (80/100), specificity = 90 % (90/100), PPV = 88.9 % (...
<p>The values indicated are weighted averages for the three classes under consideration; control, MC...
<p>ROC curves analyses to represent sensitivity/specificity of each biomarker, and the Area Under th...
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>Receiver operating characteristic (ROC) curves and corresponding areas under the curve (AUCs) com...