We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC...
A rather ugly ROC curve emerges: The area under the ROC curve, or AUC, seem like a nice heuristic to evaluate and compare the overall performance of classification models independent of the exact decision threshold chosen. But there’s more to it. Probabilistic interpretation As above, assume t...
Figure2depicts receiver operating characteristic (ROC) curves, and the corresponding areas under the ROC curves (AUC) for the twelve different models of ICH detection. Models trained on fully sampled images had AUCs of 0.898 in image-space (I1), 0.918 in sinogram-space (S1), 0.972 in windowe...
No cut-off score could be defined for the MSNQ-P because of low sensitivity. For the MSNQ-I, sensitivity was 0.75 and specificity 0.71 (AUC 0.80). The cut-off score was 21. ROC curve analyses showed no added value of the MSNQ-P when used in combination with the MSNQ-I. The MSN...
and Graphprot29on 261 static RBP binding site datasets. The area under the receiver operating characteristic curve (AUC) was adopted as the performance metric for all computational methods. For each RBP dataset, we partitioned the binding sites into training and test sets. Then, we used the te...
ROC curve (Fig. 7a) is drawn by first ranking the data based on the prediction score. Then the data are divided to intervals of equal size. The upper limit for the partitions is the number of cases in the dataset. ROC curve has on x-axis 1-specificity also called FPR and on the y...
Table 4. Area Under the ROC Curve for PIQ Item Bank T-Scores as Predictors of Itch Severity Itch severityAUC Item bank 1Item bank 2Item bank 3Item bank 4 Worst itch Moderate vs. mild 0.66 0.76 0.72 0.72 Severe vs. mild 0.82 0.92 0.87 0.85 Severe vs. moderate 0.68 0.75 0.71 0.69 Ve...
Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76-1.86]),...
The performance of the AI system on the reader study population (n = 1024 breasts) using ROC curve (a) and precision-recall curve (b). The AI achieved 0.962 (95% CI: 0.943, 0.979) AUROC and 0.752 (95% CI: 0.675, 0.849) AUPRC. Each data point represents a single reader and ...
A VGG16-based CNN model was evaluated in terms of the performance metrics including accuracy, area under the receiver operating characteristic (ROC) curve (AUC), precision, recall, F1-score, and Matthews correlation coefficient (MCC), using an independently developed testing dataset mentioned in Tab...