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Table 3 Comparison of the performance in six traditional models and the proposed DXLR model

From: Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning

Models

Metric [Mean ± SD]

Precision

Recall

Accuracy

F1 score

AUC

LR

0.538 ± 0.016

0.649 ± 0.016

0.712 ± 0.008

0.589 ± 0.013

0.766 ± 0.009

SVM

0.540 ± 0.016

0.644 ± 0.015

0.713 ± 0.008

0.587 ± 0.013

0.765 ± 0.009

DT

0.647 ± 0.015

0.827 ± 0.020

0.802 ± 0.007

0.726 ± 0.011

0.879 ± 0.006

RF

0.681 ± 0.015

0.833 ± 0.013

0.823 ± 0.007

0.749 ± 0.011

0.905 ± 0.005

XGBoost

0.714 ± 0.014

0.878 ± 0.012

0.850 ± 0.006

0.788 ± 0.010

0.928 ± 0.005

LightGBM

0.674 ± 0.015

0.836 ± 0.011

0.820 ± 0.007

0.746 ± 0.011

0.901 ± 0.005

DXLR

0.723 ± 0.014

0.892 ± 0.012

0.857 ± 0.007

0.798 ± 0.010

0.934 ± 0.004

P-valuea

< 0.0001

< 0.0001

< 0.0001

< 0.0001

< 0.0001

  1. a: t-test for the DXLR model and the best performing traditional model (XGBoost); The bold font is the best performing model