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Table 2 Comparison of performance using different numbers of variables in XGBoost TIA risk prediction models

From: Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records

Performance Metrics*

Number of Variables**

10

20

30

40

50

100

150

175

Recall

0.454

0.803

0.822

0.836

0.803

0.842

0.868

0.875

Precision

0.784

0.739

0.776

0.789

0.787

0.815

0.815

0.821

ROC-AUC

0.694

0.826

0.848

0.858

0.843

0.870

0.882

0.887

Accuracy

0.767

0.833

0.856

0.865

0.856

0.879

0.886

0.890

  1. *XGBoost base models were developed using default settings
  2. **Variables were sorted by the associated patient count in descending order, and various number of variables from the top of the list were selected for building models