Machine learning-based prediction of HCV-related cirrhosis and the exploratory analysis of drug use influence
Shuanglin Gu1, Lingling Ye2, Jiasheng Tang3
1Department of Clinical Research Center, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China.
Introduction:
Chronic hepatitis C virus (HCV) infection easily progresses to liver cirrhosis, and drug users represent a special high-risk population with accelerated liver damage and insufficient early screening strategies.
Methods:
This study aimed to develop a machine learning model for identifying HCV-related cirrhosis, with stratified analysis between drug-using and non-drug-using subgroups. We enrolled patients with chronic HCV infection and collected blood routine, liver function, lymphocyte subsets, and drug use data.
Results:
Among the seven models compared, the XGBoost model achieved the best performance, with an AUC of 0.86 on the test set. Stratified analysis showed significantly better predictive performance in drug users (AUC = 0.87) than in non-drug users (AUC = 0.72, p < 0.05). Drug use did not independently improve model performance and showed significant interactions with only a limited number of key features (e.g., ADA). SHAP and decision curve analysis validated the model's reliability and clinical benefit, especially in drug users.
Discussion:
The XGBoost model enables noninvasive and accurate cirrhosis prediction and is suitable for early screening in high-risk drug-using populations.