TABLE 4.

Prediction of massive transfusion

Method	AUC	AP	BA	Sens	Spec	Prec	NPV	F1
Neural network	0.945 (± 0.014)	0.162 (± 0.028)	0.656 (± 0.034)	0.780 (± 0.049)	0.994 (± 0.002)	0.206 (± 0.060)	0.997 (± 0.001)	0.245 (± 0.046)
Logistic regression	0.949 (± 0.012)	0.176 (± 0.021)	0.645 (± 0.042)	0.721 (± 0.055)	0.995 (± 0.002)	0.211 (± 0.032)	0.997 (± 0.000)	0.241 (± 0.031)
Random forest	0.932 (± 0.018)	0.174 (± 0.031)	0.671 (± 0.030)	0.002 (± 0.003)	0.993 (± 0.002)	0.191 (± 0.025)	0.997 (± 0.000)	0.244 (± 0.026)
Gradient boosting	0.947 (± 0.013)	0.184 (± 0.037)	0.661 (± 0.038)	0.797 (± 0.055)	0.995 (± 0.002)	0.238 (± 0.038)	0.997 (± 0.000)	0.269 (± 0.039)

Note: Statistical parameters of the prediction of massive transfusion vs no massive transfusion by different models.

Abbreviations: AP, average precision; AUC, area under the receiver operating characteristic curve; BA, balanced accuracy; F₁, harmonic mean of precision and recall; NPV, negative predictive value; Prec, precision or positive predictive value; Sens, sensitivity; Spec, specificity.

The bold values is the highest (best) for each method respectively.