[Development and validation of a machine learning-based prediction model for fatty liver in Wilson disease].

OBJECTIVES: To construct a machine learning-based predictive model for fatty liver in patients with Wilson disease (WD). METHODS: Clinical data retrospectively collected from 1862 WD patients at the First Affiliated Hospital of Anhui University of Chinese Medicine were divided into a training set (70%) and a validation set (30%). The least absolute shrinkage and selection operator (LASSO) was employed to screen the key predictive variables. Seven algorithms, namely logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector machine (SVM), and artificial neural network (ANN), were compared for their performance using the area under the receiver-operating characteristic (ROC) curve (AUC), precision-recall (PR) curve, calibration curves, and decision curve analysis (DCA). The contribution of each feature to model prediction was assessed using SHAP analysis. RESULTS: : 0.776-0.852), and the PR curve showed a high average precision (AP=0.903) with good calibration (Brier score 0.138) and significant clinical net benefit across all the diagnostic thresholds as confirmed by DCA. SHAP analysis indicated that ALT, IBIL, TBA, and CIV all had significant positive effects on model outputs, while RDW and PLT contributed minimally to the cumulative predictive outcomes. CONCLUSIONS: Among the 7 predictive models for fatty liver in WD patients, the LightGBM model demonstrates superior performance to potentially facilitate early screening and risk stratification of WD patients at high risk of fatty liver.