Fig. 2

Features selected by LASSO and RFE. (A) The Lasso regression coefficient profiles of all baseline characteristics. (B) The optimal lambda selection in the Lasso regression with 10-fold cross-validation. Misclassification errors of different variables against log(lambda) are revealed. The two vertical dashed lines represent the optimal value under the minimum criterion and 1-SE criterion, respectively. The “lambda”is the tuning parameter. (C) A total of 9 predictors with non-zero coefficients are identified. (D) Features selected by RFE, When the number of features is 10, the RMSE is the lowest. (E) The top ten significant predictors identified by RFE. (F) The Venn diagram of features selected by LASSO and RFE. The intersection results of two methods yield 7 predictors. LASSO, Least Absolute Shrinkage and Selection Operator; RFE, Recursive Feature Elimination; RMSE, Root Mean Square Error