Machine Learning-Driven Noninvasive Prediction of Liver Fibrosis in Unclassified Liver Disease.

Abstract

Chronic liver diseases (CLDs) frequently progress silently to advanced fibrosis or cirrhosis, yet existing predictive models have largely been developed for patients with clearly defined etiologies. Here, we asked whether a noninvasive machine-learning model based on routine clinical variables and liver stiffness measurement (LSM) could accurately identify significant liver fibrosis in patients with CLD of uncertain etiology, and whether such a model would add value beyond conventional fibrosis scores for clinical decision-making. We retrospectively analyzed 503 patients who underwent liver biopsy at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Twelve machine-learning algorithms were trained to predict significant hepatic fibrosis, and model interpretability was assessed using SHapley Additive exPlanations. The best-performing model was then compared with four established noninvasive tools: FIB-4, GPR, Forns Index, and LSM alone. Among all candidate models, Categorical Boosting delivered the most favorable overall diagnostic performance, achieving the highest discriminative accuracy (AUC, 0.827; 95% CI, 0.754-0.900) and outperforming all conventional comparators across key classification metrics. We therefore establish an interpretable and clinically applicable panel integrating serological markers, age, LSM for noninvasive fibrosis assessment in patients with undefined or clinically ambiguous liver disease, with potential to reduce unnecessary biopsy and enable earlier intervention.