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Table 1 Results on validation set in terms of AUROC

From: A machine learning approach for modeling decisions in the out of hospital cardiac arrest care workflow

Model

CA

CPC

LightGBM

0.7050

0.7462

Random Forest

0.6641

0.6437

XGBoost

0.6639

0.7561

Gradient Boost

0.6619

0.6977

Decision Tree

0.6415

0.6537

k-Nearest Neighbor

0.6781

0.7077

Logistic Regression

0.6937

0.7417

EFCN

0.8836

0.9272

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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