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Table 4 Performance of six ML models

From: Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults

Model

AUC

Accuracy

Sensitivity

Specificity

AdaBoost

0.825

0.723

0.751

0.695

LR

0.765

0.713

0.794

0.633

Bagging

0.814

0.747

0.715

0.727

MLP

0.773

0.717

0.802

0.633

GBM

0.857

0.766

0.798

0.734

XGBoost

0.840

0.752

0.771

0.713

  1. AUC, area under the receiver operating characteristic curve; AdaBoost, adaptive boosting; LR, logistic regression; Bagging, bootstrapped aggregating; MLP, multilayer perceptron; GBM, gradient boosting machine; and XGBoost, extreme gradient boost
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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