A machine learning model for non-invasive prediction of advanced liver fibrosis in patients with chronic hepatitis B.

PURPOSE

Chronic Hepatitis B (CHB) is a leading cause of liver fibrosis. Accurate and non-invasive diagnosis of liver fibrosis in CHB patients is of critical clinical importance. This study aimed to develop and validate machine learning (ML)-based models for predicting significant liver fibrosis in CHB patients.

METHODS

This retrospective cohort study included 328 CHB patients (225 with non-significant liver fibrosis and 103 with significant liver fibrosis) from 2017 to 2022. Four ML models were constructed based on four selected features identified through the least absolute shrinkage and selection operator (LASSO) regression. Model performance was assessed using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity, specificity, and SHapley Additive exPlanations (SHAP) analysis.

RESULTS

The random forest (RF) model demonstrated the highest predictive performance, with an AUC of 0.874 (95% CI: 0.813-0.934) in the training set and 0.863 (95% CI: 0.772-0.955) in the test set, outperforming extreme gradient boosting (XGBoost), logistic regression (LR), and support vector machine (SVM). Compared with the traditional fibrosis indices such as aspartate aminotransferase to platelet ratio index (APRI) (AUC = 0.585) and fibrosis-4 (FIB-4) (AUC = 0.633), the RF model (AUC = 0.863) demonstrated significantly higher predictive accuracy. SHAP analysis identified platelet count (PLT) as the most influential predictor in the RF model.

CONCLUSION

The ML-based RF model offers a highly accurate, non-invasive interpretable tool for predicting significant liver fibrosis in patients with CHB, offering potential for clinical application in routine fibrosis risk assessment.