Table 2.
Performance of the machine learning approaches for the estimation of 1-year in-hospital all-cause mortality.
Model | AUCa | Accuracy | Precision | Recall | F1 | Brier score |
LRb | 0.91 | 0.83 | 0.86 | 0.80 | 0.83 | 0.12 |
RFc | 1.00 | 0.97 | 0.96 | 0.98 | 0.97 | 0.03 |
SVMd | 0.99 | 0.94 | 0.93 | 0.96 | 0.94 | 0.16 |
ANNe | 0.99 | 0.97 | 0.96 | 0.98 | 0.97 | 0.03 |
XGBoostf | 0.99 | 0.94 | 0.91 | 0.98 | 0.94 | 0.05 |
aAUC: area under the curve.
bLR: logistic regression.
cRF: random forest.
dSVM: support vector machine.
eANN: artificial neural network.
fXGBoost: extreme gradient boosting.