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Table 3 Predictive abilities of the machine learning models for the asthma diagnosis after a grid search with cross-validation

From: Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method

Model

Accuracy

AUC

Sensitivity

(Recall)

Specificity

(Precision)

Tree

0.8118

0.8665

0.7193

1.0000

Random forest

0.8353

0.9060

0.7544

1.0000

XGBoost

0.8235

0.8935

0.7368

1.0000

LGBM

0.8588

0.9173

0.7895

1.0000

CatBoost

0.8235

0.9098

0.7368

1.0000

  1. LGBM Light gradient boosting model, XGBoost Extreme gradient boosting
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BMC Pulmonary Medicine

ISSN: 1471-2466

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