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Table 5 Predictive abilities of the machine learning models for the asthma diagnosis after bootstrap 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)

F1 score

XGBoost

0.869421

0.893428

0.823082

0.978909

0.89388

LGBM

0.858168

0.894258

0.788516

1.0000

0.881333

(Logi)

0.858168

0.894258

0.788516

1.0000

0.881333

Tree

0.834608

0.849462

0.80581

0.938875

0.86682

Random forest

0.822236

0.867463

0.734925

1.0000

0.846677

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

ISSN: 1471-2466

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