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Table 2 Prediction performance of different machine learning models

From: Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management

 

AUROC

Accuracy

Precision

Recall

F1-score

Logistic Regression

0.74 (0.71—0.77)

0.67 (0.64, 0.69)

0.31 (0.28, 0.35)

0.70 (0.64, 0.74)

0.43 (0.39, 0.47)

Random Forest

0.71 (0.68—0.74)

0.72 (0.70, 0.74)

0.34 (0.30, 0.38)

0.60 (0.55, 0.66)

0.44 (0.40, 0.48)

XGBoost

0.72 (0.69—0.75)

0.82 (0.80, 0.84)

0.65 (0.37, 0.91)

0.03 (0.01, 0.05)

0.06 (0.03, 0.10)

Neural Network

0.72 (0.68—0.75)

0.82 (0.81, 0.84)

0.65 (0.47, 0.83)

0.06 (0.04, 0.09)

0.11 (0.07, 0.16)

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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