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Based on the ROC curve, we consider that a test statistic whose ROC curve lies below that of another test statistic has better performance.
Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index).
We have further evaluated the models based on the ROC AUCs that represent more accurately the performance of the models.
Cutoff values for CK, sMb, and UMb were calculated based on the ROC curve corresponding to the simple logistic regression models.
We dichotomized values of creatinine (using a renal SOFA cut-off of 1.2 mg/dL) and endocan (best sensitivity and specificity based on the ROC curve) to predict need for RRT.
Based on the ROC curves, we evaluated the performances of the proposed LRT VAD based on the MP coefficients (LRT-MP) by comparing with the popular LRT VADs based on DFT coefficients, including Gaussian (LRT-Gaussian) [7], Laplacian (LRT-Laplacian) [8], and Gamma (LRT-Gamma) [10].
In every fold of the cross validation, the best set of 20 channels is chosen based on the ROC value.
The best accuracy for LBP was detected at 26 236 ng/L based on the ROC analysis with a sensitivity of 60.2% and a specificity of 75.1%.
For outcome prediction in breast cancer, the True Positive Rate TPRR) is typically set at a desired threshold, and based on the ROC curve, the corresponding False Positive Rate (FPR) possible is determined.
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Based on the ROC-curve, sensitivity and specificity were calculated for various cut-off scores.
Based on the ROC-analyses, we decided to choose ≥4 and ≥10 as the revised lower and higher cut-off points for the 4DSQ anxiety scale.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com