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Table 2 Model performance metrics

From: Machine learning for the prediction of acute kidney injury in patients with sepsis

Models

AUC

Recall

Accuracy

F1 score

Sensitivity

Specificity

LR

0.737

0.796

0.765

0.858

0.834

0.878

KNN

0.664

0.798

0.742

0.840

0.886

0.857

SVM

0.735

0.797

0.788

0.874

0.833

0.926

Decision tree

0.749

0.834

0.793

0.870

0.910

0.882

Random forest

0.779

0.809

0.794

0.876

0.935

0.923

XGBoost

0.817

0.852

0.832

0.895

0.943

0.913

ANN

0.755

0.778

0.783

0.875

0.824

0.899

SOFA

0.646

0.755

0.723

0.781

0.633

0.712

SAPS II

0.702

0.774

0.762

0.814

0.811

0.845

  1. AUC area under curve, LR logistic regression KNN: k-nearest neighbors, SVM support vector machine, XGBoost extreme gradient boosting, ANN artificial neural network, SOFA Sequential Organ Failure Assessment, SAPS II the Simplified Acute Physiology Score II
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Journal of Translational Medicine

ISSN: 1479-5876

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