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Table 4 Performances metrics for LR, SVM, DT, RF, LDA, and QDA algorithms

From: Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

  Models Accuracy Sensitivity Specificity Kappa AUC-ROC AUC-PR
Total SVM 0.725 0.661 0.785 0.447 0.785 0.761
DT 0.738 0.667 0.804 0.473 0.771 0.730
RF 0.743 0.699 0.784 0.484 0.804 0.776
LR 0.705 0.677 0.732 0.409 0.770 0.748
LDA 0.562 0.915 0.230 0.141 0.658 0.666
QDA 0.546 0.492 0.598 0.089 0.563 0.555
Male SVM 0.712 0.475 0.870 0.366 0.733 0.766
DT 0.735 0.527 0.874 0.421 0.739 0.753
RF 0.729 0.559 0.842 0.415 0.754 0.782
LR 0.711 0.519 0.839 0.373 0.732 0.768
LDA 0.591 0.754 0.482 0.217 0.679 0.734
QDA 0.547 0.394 0.649 0.044 0.531 0.616
Female SVM 0.733 0.783 0.671 0.456 0.802 0.565
DT 0.748 0.753 0.742 0.492 0.785 0.706
RF 0.744 0.767 0.715 0.482 0.815 0.754
LR 0.738 0.798 0.663 0.465 0.803 0.741
LDA 0.608 0.752 0.427 0.184 0.664 0.617
QDA 0.635 0.749 0.491 0.245 0.670 0.572
  1. LR Logistic Regression, SVM support vector machines, DT Decision Tree, RF Random Forest, LDA Linear discriminant analysis, QDA Quadratic discriminant analysis, AUC Area Under Curve. Machine learning methods outperforms the traditional statistical methods