<p>Sensitivity and specificity was maximized at a sensitivity of 0.858 and specificity of 0.623.</p
<p>The x-axis is the false- positive rate, 1 –specificity; the y-axis is the true- positive rate, se...
<p><b>A)</b> ROC curves showing sensitivity and specificity for individual biomarkers. <b>B)</b> Pre...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
Sensitivity verse 1-Specificity. Dotted lines are baseline model RiskOP model, solid lines are the n...
The abscissa indicates the sensitivity, while the ordinate indicates 1-specificity. At a cut-off of ...
<p>The curve presents the true positive rate (or sensitivity) in function of false positive rate for...
<p>Note: the threshold at fixed specificity/sensitivity achieved by doctors are used to calculate th...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Fig 3a. ROC curves for different biomarkers and PSI. Fig 3b. ROC curves for the PSI & MR-proADM p...
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).</p
<p>The ROC AUC was 0.7686. The straight line represented the ROC curve expected by chance alone.</p
<p>The x-axis is the false- positive rate, 1 –specificity; the y-axis is the true- positive rate, se...
<p><b>A)</b> ROC curves showing sensitivity and specificity for individual biomarkers. <b>B)</b> Pre...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
<p>The solid curve is the ROC curve for the prediction model established on the basis of the trainin...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
Sensitivity verse 1-Specificity. Dotted lines are baseline model RiskOP model, solid lines are the n...
The abscissa indicates the sensitivity, while the ordinate indicates 1-specificity. At a cut-off of ...
<p>The curve presents the true positive rate (or sensitivity) in function of false positive rate for...
<p>Note: the threshold at fixed specificity/sensitivity achieved by doctors are used to calculate th...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Fig 3a. ROC curves for different biomarkers and PSI. Fig 3b. ROC curves for the PSI & MR-proADM p...
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
ROC curve showing optimal cut-off value of 3 mm (sensitivity, 0.795; specificity, 0.61).</p
<p>The ROC AUC was 0.7686. The straight line represented the ROC curve expected by chance alone.</p
<p>The x-axis is the false- positive rate, 1 –specificity; the y-axis is the true- positive rate, se...
<p><b>A)</b> ROC curves showing sensitivity and specificity for individual biomarkers. <b>B)</b> Pre...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
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