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Table 5 Accuracy for in-hospital mortality

From: The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis

No

Model

Training cohort

Validation cohort

Number of models

Accuracy (95% CI)

Number of models

ACC (95% CI)

1

LR

15

0.8302 [0.7800; 0.8709]

16

0.8487 [0.8250; 0.8697]

2

RF

6

0.8106 [0.7462; 0.8617]

12

0.8881 [0.8639; 0.9084]

3

ANN

3

0.8864 [0.7927; 0.9409]

2

0.8651 [0.8364; 0.8893]

4

DT

4

0.8824 [0.8094; 0.9299]

2

0.8464 [0.7164; 0.9232]

5

SVM

4

0.7902 [0.7333; 0.8376]

6

0.8199 [0.7772; 0.8559]

6

XGBoost

6

0.8679 [0.8033; 0.9136]

4

0.8505 [0.8332; 0.8663]

7

NB

1

0.7984 [0.7892; 0.8073]

2

0.7615 [0.7342; 0.7869]

8

KNN

  

1

0.9112 [0.8973; 0.9233]

9

Other

3

0.8800 [0.7551; 0.9458]

  

10

Overall

42

0.8434 [0.8166; 0.8669]

45

0.8569 [0.8411; 0.8715]