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TABLE 6 Performance of optimized ML methods

From: Improvement of APACHE II score system for disease severity based on XGBoost algorithm

Abbreviation

Accuracy

[95% Cl]

Precision

[95% Cl]

Recall

[95% Cl]

F1

[95% Cl]

SVM*

0.774 ± 0.003

[0.770, 0.779]

0.826 ± 0.004

[0.820, 0.832]

0.774 ± 0.003

[0.770, 0.779]

0.794 ± 0.003

[0.790, 0.798]

LR*

0.739 ± 0.004

[0.734, 0.744]

0.840 ± 0.004

[0.834, 0.846]

0.739 ± 0.004

[0.734, 0.744]

0.770 ± 0.003

[0.766, 0.775]

NB*

0.840 ± 0.005

[0.833, 0.847]

0.814 ± 0.007

[0.805, 0.824]

0.840 ± 0.005

[0.833, 0.847]

0.822 ± 0.006

[0.815, 0.830]

ANN*

0.863 ± 0.004

[0.856, 0.869]

0.841 ± 0.009

[0.829, 0.853]

0.863 ± 0.005

[0.856, 0.869]

0.832 ± 0.007

[0.823, 0.841]

RF*

0.857 ± 0.003

[0.853, 0.862]

0.859 ± 0.003

[0.854, 0.863]

0.859 ± 0.003

[0.855, 0.864]

0.860 ± 0.004

[0.855, 0.865]

XGBoost*

0.867 ± 0.004

[0.860, 0.872]

0.846 ± 0.007

[0.836, 0.856]

0.867 ± 0.004

[0.860, 0.872]

0.841 ± 0.006

[0.832, 0.850]

  1. * The data set used for model building contains 19 feature variables