Artificial intelligence for early prediction of alcohol-related liver disease: Advances, challenges, and clinical applications

Table 2 Comparative analysis of artificial intelligence models in alcohol-related liver disease prediction

AI model	Methodology	Performance metrics	Key limitations	Citation
Gradient Boosting	MICE imputation, SMOTE, feature selection	AUC = 0.87 (30-day mortality prediction)	Small sample size, Lack of external validation, missing data	Gao et al[2], 2022
Stacked Ensemble (XGBoost + Logistic Regression)	Multi-omics + clinical features	Accuracy = 93.86% (alcoholic cirrhosis prediction)	Lack of external validation	Vinutha et al[6], 2022
Random Forest/XGBoost	Bayesian optimization	Accuracy = 81.06% (Random Forest), 79.85% (XGBoost)	Data heterogeneity	Kumar and Rani[7], 2024
Extra Tree with Oversampling	Liver stiffness + clinical data	Accuracy = 92% (early ARLD detection)	Requires high-resolution imaging	Lima et al[10], 2024

AUC: Area under the curve; MICE: Multivariate imputation by chained equations; SMOTE: Synthetic minority oversampling technique; XGBoost: eXtreme Gradient Boosting; ARLD: Alcohol-related liver disease; AI: Artificial intelligence.