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Table 4 Predictive performance comparison of the five types of machine learning algorithms in the validation sets

From: Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

Model

AUROC

Sensitivity

Specificity

Accuracy

LR

0.91

0.86

0.74

0.80

XGBoost

0.87

0.95

0.78

0.86

DT

0.76

0.82

0.70

0.76

GBM

0.82

0.82

0.78

0.80

RF

0.95

1.00

0.78

0.88

  1. Abbreviations: LR Logistic regression, GBM Gradient boosting machine, RF Random forest, DT Decision tree, XGBoost Extreme gradient boosting
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BMC Geriatrics

ISSN: 1471-2318

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