Table 2 Performance comparison of the machine learning models in the validation set. 

From: Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units

Models

AUC

Balanced accuracy

Sensitivity

Specificity

PPV

NPV

Kappa

SVM

0.753

0.701

0.587

0.816

0.433

0.587

0.355

XGBoost

0.841

0.778

0.845

0.711

0.412

0.950

0.397

DT

0.772

0.751

0.781

0.721

0.402

0.932

0.370

RF

0.835

0.661

0.413

0.909

0.520

0.866

0.349

LR

0.830

0.764

0.890

0.638

0.371

0.960

0.345

KNN

0.516

0.516

0.065

0.967

0.323

0.812

0.046

ANN

0.644

0.501

0.903

0.099

0.194

0.810

0.001

  1. SVM: support vector machine; xgboost: extreme gradient boosting; LR: logistic regression; KNN, k-nearest neighbors; ANN: artificial neural network; AUC: area under the curve.
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