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ROC curves were generated by plotting sensitivity against one-specificity.
The ROC curves capture this behavior by plotting "sensitivity" against "FPR (1- specificity, which indicates false alarms)" for the different possible probability thresholds of a classification model [9, 38, 68, 95].
The goodness of fit was assessed by the Hosmer and Lemeshow test and receiver operating characteristic (ROC) curve, which was created by plotting sensitivity against (1- specificity) to establish the accuracy of predictions.
For each of 4×4 combinations of the parameters, an ROC curve was produced by plotting sensitivity against 1-specificity in detecting the preset informative voxels at different thresholds of discriminative weights.
ROC curves were generated by plotting sensitivity against 1-specificity.
We constructed receiver operating characteristic (ROC) curves by plotting sensitivity against 1 – specificity.
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It is created by plotting sensitivity (true positive rate) against 1-specificity (false positive rate) at various threshold settings.
Coupling the anchors to the outcome measures (ΔOHS and absolute OHS), sensitivity and specificity for different threshold values were assessed by receiver operating characteristic (ROC) curves plotting sensitivity against specificity.
A ROC-curve is obtained by calculating the sensitivity and specificity of every observed data value and plotting sensitivity against 1-specificity.
Initially, these will be plotted as a coupled forest plot of sensitivity and specificity, and a scatter plot in ROC space (plotting sensitivity against 1 − specficity for each study).
By grading test results according to five categories (strongly positive, 5; weakly positive, 4; intermediate, 3; weakly negative, 2; strongly negative, 1) and plotting sensitivity against 1 specificity, the ROC curve is generated (Fig. 1).
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