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Table 6 Bootstrap Evaluation of Model Performance

From: Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH

 

LR

SVM

XGBoost

RF

KNN

Tab Transformer

Accuracy

0.818

[0.768,0.868]

0.810 [0.759,0.859]

0.822 [0.768,0.868]

0.837 [0.786,0.886]

0.833 [0.782,0.882]

0.815 [0.786,0.841]

AUC

0.846

[0.786,0.911]

0.821 [0.768,0.870]

0.880 [0.834,0.923]

0.845 [0.796,0.894]

0.792 [0.726,0.857]

0.839 [0.775,0.902]

  1. AUC Area under the receiver operating characteristic curve, LR multinomial linear regression, SVM support vector machine, XGBoost Extreme Gradient Boosting, RF random forest, KNN K-Nearest Neighbors, Tab Transformer Tabular Transformer
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Respiratory Research

ISSN: 1465-993X

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