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Table 2 Mortality prediction models for patients undergoing continuous renal replacement therapy in the test set

From: Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Models

AUC (95% CI)

P value*

P value

P value

Accuracy

F1 score

APACHE II

0.611 (0.583–0.640)

   

0.607

0.660

SOFA

0.677 (0.651–0.703)

   

0.629

0.643

MOSAIC

0.722 (0.677–0.767)

   

0.660

0.658

κ-Nearest neighbor

0.762 (0.719–0.805)

< 0.001

< 0.001

0.213

0.673

0.745

Support vector machine

0.771 (0.729–0.813)

< 0.001

< 0.001

0.119

0.692

0.752

Multivariate adaptive regression splines

0.753 (0.710–0.796)

< 0.001

0.003

0.332

0.673

0.736

Random forest

0.784 (0.744–0.825)

< 0.001

< 0.001

0.045

0.690

0.762

Extreme gradient boost

0.776 (0.735–0.818)

< 0.001

< 0.001

0.085

0.715

0.763

Artificial neural network

0.776 (0.735–0.818)

< 0.001

< 0.001

0.082

0.694

0.749

  1. Abbreviations: AUC area under the curve, CI confidence interval, APACHE Acute Physiology and Chronic Health Evaluation, SOFA Sequential Organ Failure Assessment, MOSAIC Mortality Scoring system for AKI with CRRT
  2. *Compared with the APACHE II model
  3. Compared with the SOFA model
  4. Compared with the MOSAIC model