TABLE 2.

Prediction of transfusion

Method	AUC	AP	BA	Sens	Spec	Prec	NPV	F1
Neural network	0.966 (± 0.004)	0.828 (± 0.012)	0.870 (± 0.008)	0.898 (± 0.007)	0.958 (± 0.009)	0.719 (± 0.022)	0.970 (± 0.006)	0.749 (± 0.006)
Logistic regression	0.965 (± 0.005)	0.820 (± 0.011)	0.856 (± 0.006)	0.894 (± 0.012)	0.966 (± 0.009)	0.749 (± 0.008)	0.966 (± 0.004)	0.748 (± 0.010)
Random forest	0.963 (± 0.004)	0.821 (± 0.011)	0.858 (± 0.004)	0.584 (± 0.006)	0.964 (± 0.006)	0.737 (± 0.011)	0.966 (± 0.006)	0.743 (± 0.006)
Gradient boosting	0.966 (± 0.003)	0.835 (± 0.013)	0.864 (± 0.008)	0.872 (± 0.006)	0.965 (± 0.005)	0.747 (± 0.025)	0.968 (± 0.007)	0.755 (± 0.007)

Note: Statistical parameters of the prediction of transfusion vs no 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.