Table 4 Accuracies, sensitivities, specificities, and area under the curve (AUC) values of prediction models in an independent validation.

From: Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis

Machine learning algorithm

Accuracy

Sensitivity

Specificity

AUC

LR

0.746

0.756

0.727

0.755

SVM

0.746

0.844

0.545

0.731

SNN

0.716

0.867

0.409

0.707

RF

0.806

0.822

0.773

0.800

NB

0.776

0.822

0.682

0.743

Mean ± SD

0.758 ± 0.034

0.822 ± 0.042

0.627 ± 0.149

0.747 ± 0.034

95% CI

0.716–0.800

0.771–0.874

0.443–0.812

0.705–0.790

  1. LR: logistic regression, SVM: support vector machine, SNN: standard neural network, RF: random forest, NB: naïve Bayes.
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