Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm

Figure 1 The main procedure of the random forest-backward feature elimination algorithm. BFE: Backward feature elimination; LASSO: Least absolute shrinkage and selection operator.

Figure 2 Feature selection scheme in an iteration. LASSO: Least absolute shrinkage and selection operator.

Figure 3 istogram of the votes in the feature counter. BMI: Body mass index; HBsAg: Hepatitis B surface antigen; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; TC: Total cholesterol; TBil: Total bilirubin; DBil: Direct bilirubin; PT: Prothrombin time; Cr: Creatinine; BUN: Blood urea nitrogen; qAnti-HBc: Quantitative hepatitis B core antibody; IL: Interleukin, IFN: Interferon; TNF: Tumor necrosis factor; TGF: Transforming growth factor.

Figure 4 Receiver operating characteristic curve analysis for predicting moderate-to-severe inflammation in all patients with chronic hepatitis B virus infection, HBeAg-positive patients with chronic hepatitis B virus infection, and HBeAg-negative patients with chronic hepatitis B virus infection. A: All patients with chronic hepatitis B virus infection; B: HBeAg-positive patients with chronic hepatitis B virus infection; C: HBeAg-negative patients with chronic hepatitis B virus infection. AUROC: Area under the receiver operating characteristics curve; CI: Confidence interval; qAnti-HBc: Quantitative hepatitis B core antibody.