<p>ROC curves for the best result in Experiment 4 (Non correlated features at 97%).</p
<p>ROC curves for the determination of the overall performance of the assay and the optimal cut-off ...
<p>ROC curves of the APPCI, FI, APRI, GPI, and APGA noninvasive models in all study subjects.</p
<p>(A) Results when validating against the neutral control set. (B) Results when validating against ...
ROC curves of the model under non-feature selection, feature selection, and mixed sampling.</p
<p>ROC curve for the best model found on the reduced feature set (discovery).</p
<p>ROC curve for the best model found on the reduced feature set (replication).</p
ROC curves for the top performing model compared to individual feature predictions.</p
<p>ROC curve for logistic regression, the best model found on the reduced feature set (discovery).</...
<p>The ROC curves for evaluating the quality of the algorithms over the entire test datasets.</p
<p>Panel A gives the ROC curves at each possible control of false positive rate, while panel B only ...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
<p>ROC curves obtained for the best and worst combinations of sensor positions and algorithms for th...
<p> <b>ROC curves for four different classifiers and the set of features selected at t...
The ROC curves for 2-folds, 4-folds, 10-folds, and leave one subject out cross validation experiment...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>ROC curves for the determination of the overall performance of the assay and the optimal cut-off ...
<p>ROC curves of the APPCI, FI, APRI, GPI, and APGA noninvasive models in all study subjects.</p
<p>(A) Results when validating against the neutral control set. (B) Results when validating against ...
ROC curves of the model under non-feature selection, feature selection, and mixed sampling.</p
<p>ROC curve for the best model found on the reduced feature set (discovery).</p
<p>ROC curve for the best model found on the reduced feature set (replication).</p
ROC curves for the top performing model compared to individual feature predictions.</p
<p>ROC curve for logistic regression, the best model found on the reduced feature set (discovery).</...
<p>The ROC curves for evaluating the quality of the algorithms over the entire test datasets.</p
<p>Panel A gives the ROC curves at each possible control of false positive rate, while panel B only ...
<p>ROC curve and prediction parameters for optimal thresholds in all tested methods.</p
<p>ROC curves obtained for the best and worst combinations of sensor positions and algorithms for th...
<p> <b>ROC curves for four different classifiers and the set of features selected at t...
The ROC curves for 2-folds, 4-folds, 10-folds, and leave one subject out cross validation experiment...
<p>A ROC curve plots the true positive rate (i.e., sensitivity) against the false positive rate (i.e...
<p>ROC curves for the determination of the overall performance of the assay and the optimal cut-off ...
<p>ROC curves of the APPCI, FI, APRI, GPI, and APGA noninvasive models in all study subjects.</p
<p>(A) Results when validating against the neutral control set. (B) Results when validating against ...
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