Where, X-axis (1-specificity) represents the false positives in decoys. Y-axis (sensitivity) represents the (true +ves in the decoys).</p
<p>ROC analysis of sensitivity versus 1-specificity in the disease diagnosis for proposed measuremen...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Sensitivity and specificity was maximized at a sensitivity of 0.858 and specificity of 0.623.</p
<p>The curve presents the true positive rate (or sensitivity) in function of false positive rate for...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).</p
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
<p>ROC curve modeling the sensitivity versus 1-specificity for variables diagnosing the existence of...
<p>Roc-curves describing the relation between sensitivity and specificity of conscious/unconscious d...
<p>ROC curves (a plot of true positive rate (Sensitivity) against false positive rate (1-Specificity...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>Displaying the tradeoff between the sensitivity (true positive rate) and the 1-specificity (false...
<p>a) IL-10 ROC curve, b) IL-6 ROC curve, c) IL-10/IL-6 ROC curve. The vertical axis represents the ...
<p>The blue line indicates the prediction scenario using only clinical variables (hippocampal sclero...
<p>NB: AUC–Area under curve denotes the area in the graph below Receiver Operating Characteristic (R...
<p>ROC analysis of sensitivity versus 1-specificity in the disease diagnosis for proposed measuremen...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Sensitivity and specificity was maximized at a sensitivity of 0.858 and specificity of 0.623.</p
<p>The curve presents the true positive rate (or sensitivity) in function of false positive rate for...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>ROC curve analysis: Plot of sensitivity versus 1-specificity (PD-Q scale).</p
<p>ROC-curve for the Whiteley Index and sensitivity/specificity as a function of probability cut-off...
<p>ROC curve modeling the sensitivity versus 1-specificity for variables diagnosing the existence of...
<p>Roc-curves describing the relation between sensitivity and specificity of conscious/unconscious d...
<p>ROC curves (a plot of true positive rate (Sensitivity) against false positive rate (1-Specificity...
<p>AUC: area under the curve, FPF: false positive fraction, TNF: true negative fraction, TPF: true p...
<p>Displaying the tradeoff between the sensitivity (true positive rate) and the 1-specificity (false...
<p>a) IL-10 ROC curve, b) IL-6 ROC curve, c) IL-10/IL-6 ROC curve. The vertical axis represents the ...
<p>The blue line indicates the prediction scenario using only clinical variables (hippocampal sclero...
<p>NB: AUC–Area under curve denotes the area in the graph below Receiver Operating Characteristic (R...
<p>ROC analysis of sensitivity versus 1-specificity in the disease diagnosis for proposed measuremen...
Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...
<p>Sensitivity and specificity was maximized at a sensitivity of 0.858 and specificity of 0.623.</p
DOI
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