5 × 5-fold cross-validation results: Mean ROC curves and AUC scores (95% C.I.).</p
<p>(A) ROC curves based on the three orthogonal ontologies of GO. The maximum AUC score was 0.71 whe...
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
<p>The areas under the ROC curve (AUCs) for the CIRP level, the APACHE II score, the SOFA score, the...
We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across fol...
Box extends from the Q1 to Q3 quartile values of the data, with a line at the median and a triangle ...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
<p>Results are evaluated based on the benchmark dataset (A) and independent test dataset (B).</p
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>Mean ROC curves for each cross-validation and global ROC, when features appearing more than 18 ti...
The best score for each species model is highlighted in green. Models are displayed vertically in ro...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>The ROC curves of the RF and SVM in internal five-fold cross validation for (a) Model I, (b) Mode...
<p>As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...
<p>(A) ROC curves based on the three orthogonal ontologies of GO. The maximum AUC score was 0.71 whe...
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
<p>The areas under the ROC curve (AUCs) for the CIRP level, the APACHE II score, the SOFA score, the...
We represent each fold of our subjectwise 10-fold cross-validation with a separate curve. Across fol...
Box extends from the Q1 to Q3 quartile values of the data, with a line at the median and a triangle ...
<p>The ROC curve for leave-one-out cross validation and the AUC of our algorithm is 0.7645.</p
<p>The model gives a high receiver operating curve (AUC) of 0.9277 for the training set and 0.9900 f...
<p>Results are evaluated based on the benchmark dataset (A) and independent test dataset (B).</p
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....
Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...
<p>Mean ROC curves for each cross-validation and global ROC, when features appearing more than 18 ti...
The best score for each species model is highlighted in green. Models are displayed vertically in ro...
The ROC curves in the training (A), internal validation (B) and external validation (C) groups. The ...
<p>The ROC curves of the RF and SVM in internal five-fold cross validation for (a) Model I, (b) Mode...
<p>As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...
<p>(A) ROC curves based on the three orthogonal ontologies of GO. The maximum AUC score was 0.71 whe...
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) wi...
<p>The areas under the ROC curve (AUCs) for the CIRP level, the APACHE II score, the SOFA score, the...
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