Construction and evaluation of a liver cancer risk prediction model based on machine learning

Figure 2 Revising modeling parameters in the training set. A: Ten-fold cross-validation for tuning parameter selection in the least absolute shrinkage and selection operator (LASSO) model; B: LASSO coefficient curve of 9 variables; C: The relationship between the quantity of decision trees and the average out-of-bag evaluation; D: The ranking of variable importance based on differences in univariate analysis. WBC: White blood cell; RBC: Red blood cell; PLT: Platelet; AFP: Alpha-fetoprotein; PIVKA: Protein induced by vitamin K absence or antagonist; CA: Carbohydrate antigen; CEA: Carcinoembryonic antigen; HB: Hemoglobin level.