Exploring Explainable Machine Learning in Early Liver Disease Detection: Insights from Fatty Liver and Hepatitis B

This paper investigates the integration of Explainable Machine Learning (XAI) techniques in the early detection of Fatty Liver Disease (FLD) and Hepatitis B Virus (HBV) infections. We analyze contemporary XAI methods such as SHAP, LIME, and Anchors to enhance the interpretability of complex machine learning models in medical diagnostics. Our review underscores the importance of embedding explainability to ensure accurate diagnoses, foster clinician trust, and improve patient outcomes. XAI techniques demystify “black box” models, aligning algorithmic insights with medical expertise. We also examine the influence of XAI on the adoption of AI in healthcare and the development of reliable diagnostic tools. This work lays the foundation for future research focused on improving model generalizability, addressing ethical considerations, and achieving real-time integration into clinical practice. Adopting explainable models for liver disease diagnosis is crucial, and ongoing innovation in XAI techniques is essential to meet the evolving demands of healthcare.