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. Author manuscript; available in PMC: 2024 Mar 18.
Published in final edited form as: Thorax. 2024 Mar 15;79(4):307–315. doi: 10.1136/thorax-2023-220226

Table 2.

Area under the receiver operating characteristic curve (AUC) based on the K-fold cross-validation of three different machine learning classification models for nodule malignancy prediction based on epidemiologic and radiomic features. We present the cross-validated AUC and confidence intervals.

ML Model Optimal hyperparameters CV-AUC (95% CI)
XGBoost Num. of trees = 149
Tree depth = 11
Minimum node size = 15
Num. of predictors = 452
Learning rate = 0.0673
Loss reduction = 4.315
0.933 (0.923-0.944)
LASSO 1 Penalty = 0.00044 0.930 (0.914-0.946)
Random Forest Num. of trees = 147
Num. of predictors = 53
Minimum node size = 26
0.916 (0.904-0.929)

Abbreviations: AUC, area under the curve; CI, confidence interval; LASSO, least absolute shrinkage and selection operator; ML, machine learning; Num, number; XGBoost, eXtreme Gradient Boosting.

1

The penalty parameter for the LASSO model was a L1 (i.e., LASSO) penalty.