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Table 3 Delong test for comparison of AUC of different models

From: Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV)

Dataset

Models

AUC (95%CI)

Statistics

P

Training set

Random forest

0.818 (0.801–0.835)

6.71

< 0.001

XGBoost

0.827 (0.811–0.843)

3.58

< 0.001

LGBM

0.811 (0.794–0.829)

9.43

< 0.001

KNN

0.824 (0.808–0.840)

2.99

0.003

Stacking ensemble

0.837(0.821–0.852)

Ref

 

Testing set

Random Forest

0.747 (0.717–0.777)

4.08

< 0.001

XGBoost

0.755 (0.725–0.785)

2.52

0.012

LGBM

0.754 (0.724–0.784)

2.86

0.004

KNN

0.746 (0.716–0.776)

2.61

0.009

Stacking ensemble

0.768(0.740–0.796)

Ref

 
  1. Note AUC, the area under of the curve; CI, confidence interval; XGBoost, eXtreme Gradient Boosting; LGBM, Light Gradient Boosting Machine; KNN, K-Nearest Neighbor
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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