Revolutionizing Wilson disease prognosis: a machine learning approach to predict acute-on-chronic liver failure.

ABSTRACT:

Background and objectives

Wilson disease (WD), an inherited copper metabolism disorder, is a cause of acute-on-chronic liver failure (ACLF), posing life-threatening risks due to rapid progression. This study aimed to develop a machine learning (ML)-based model to predict ACLF risk in WD patients.

Methods

We retrospectively analyzed 3692 WD patients (Leipzig score ≥ 4) from The First Affiliated Hospital of Anhui University of Chinese Medicine (2014-2024), including 104 ACLF and 104 non-ACLF cases. The original data set was randomly divided into the training and test cohorts in a ratio of 7:3. Demographic, biochemical, and ultrasound data were collected. Six ML algorithms (LR, SVM, KNN, ExtraTrees, XGBoost, LightGBM) were applied to construct a predictive model, with SHAP expl

SUBMITTER: Rao Z 

PROVIDER: S-EPMC12462144 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature