Table 2. Performance of scoring systems and ML approaches for the estimation of in-hospital mortality in patients with an out-of-hospital cardiac arrest.

Model	Predicted mortality	AUC (95% CI)	Brier score	Log loss
Actual mortality	45.5%
APACHE III risk of death	52.8%	0.80 (0.79–0.82)	0.190	0.57
ANZROD	39.9%	0.81 (0.80–0.82)	0.182	0.55
Logistic regression	45.4%	0.82 (0.81–0.83)	0.170	0.51
Artificial neural network	46.7%	0.85 (0.84–0.86)	0.158	0.48
Random forest	45.7%	0.86 (0.84–0.87)	0.156	0.47
Support vector classifier	45.4%	0.86 (0.85–0.87)	0.153	0.47
Ensemble	45.5%	0.87 (0.86–0.88)	0.148	0.45
Gradient boosted machine	45.3%	0.87 (0.86–0.88)	0.147	0.45

Results presented are based on test set (n = 3,957).

ANZROD, Australian and New Zealand Risk of Death; AUC, area under the curve; ML, machine learning.