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Table 2 Performance evaluation of four machine learning algorithms

From: Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis

 

CE

AUC

Accuracy

Specificity

KNN

0.2903

0.7653

0.7097

0.8788

LR

0.2290

0.7993

0.7710

0.8981

Ranger

0.2518

0.7896

0.7482

0.8998

Xgboost

0.2632

0.7583

0.7368

0.8577

  1. AUC, Area under the curve; CE, Classification error; Xgboost, Extremely gradient boosting machine; KNN, K-nearest neighbor; LR, Logistic regression
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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