Fig 1.
LR: Logistic regression; RF: Random forest; XGBoost: eXtreme Gradient Boosting; KNN: k-nearest neighbors; SVM: Support vector machine; MLP: Multilayer perceptron.
Table 1.
Baseline characteristics.
Fig 2.
Pearson correlation coefficient.
Fig 3.
(A) Regression coefficient variation curve based on LASSO. (B) The optimal λ process was obtained through iterative analysis using the 10-fold cross-validation method based on LASSO. (C) Feature variable screening based on RF-RFE. (D) LASSO combination RF-RFE. LASSO: Least Absolute Contraction and Selection Operator; RF: Random Forest; RFE: Recursive Feature Elimination.
Table 2.
Performance parameters of the six prediction models.
Fig 4.
The mean ROC curves of the six models.
AUC: area under the curve; LR: Logistic regression; RF: Random forest; XGBoost: eXtreme Gradient Boosting; KNN: k-nearest neighbors; SVM: Support vector machine; MLP: Multilayer perceptron.