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• his study addresses the critical challenge of developing highly accurate yet interpretable machine learning models for liver disease prediction. Using a dataset of 1700 patient records with 10 predictive features, we develop and evaluate multiple machine learning and deep learning approaches. Our en

semble-based Boosting Classifier achieves superior accuracy (90.88%) compared to traditional methods and recent studies. To overcome the “black-box” limitation of complex models, we integrate SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) techniques, providing both global and individual-level insights into model decisions. The interpretability analysis reveals Liver Function Test, Alcohol Consumption, and Age as the most significant pr