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Table 4 Performance of 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 0.736 ± 0.003
[0.732, 0.740]
0.819 ± 0.005
[0.813, 0.825]
0.736 ± 0.003
[0.732, 0.740]
0.765 ± 0.002
[0.763, 0.767]
LR 0.704 ± 0.004
[0.698, 0.710]
0.822 ± 0.003
[0.817, 0.826]
0.704 ± 0.004
[0.698, 0.710]
0.742 ± 0.003
[0.737, 0.746]
NB 0.841 ± 0.005
[0.834, 0.847]
0.802 ± 0.007
[0.792, 0.812]
0.841 ± 0.005
[0.834, 0.847]
0.809 ± 0.004
[0.803, 0.814]
ANN 0.854 ± 0.002
[0.851, 0.858]
0.827 ± 0.007
[0.817, 0.837]
0.854 ± 0.002
[0.851, 0.858]
0.810 ± 0.004
[0.805, 0.815]
RF 0.841 ± 0.003
[0.837, 0.845]
0.840 ± 0.002
[0.837, 0.843]
0.840 ± 0.001
[0.839, 0.841]
0.841 ± 0.002
[0.838, 0.843]
XGBoost 0.858 ± 0.002
[0.855, 0.862]
0.834 ± 0.005
[0.827, 0.841]
0.858 ± 0.002
[0.855, 0.862]
0.824 ± 0.003
[0.820, 0.829]
Apache II 0.742 ± 0.000 0.796 ± 0.000 0.742 ± 0.000 0.764 ± 0.000