Review About the Application of Explainable Machine Learning for Early Detection and Diagnosis of Liver Diseases: A Case Study on Fatty Liver Disease (FLD) and Hepatitis B (HBV)

Abstract

This chapter explores integrating XML techniques into early detection of Fatty Liver Disease (FLD) and Hepatitis B Virus (HBV) infections. We analyze contemporary XML methods (SHAP, LIME, Anchors) to improve interpretability of complex machine learning models in medical diagnostics. Our review emphasizes embedding explainability for accurate diagnoses, clinician trust, and improved patient outcomes. XML demystifies “black box” models, aligning insights with medical knowledge. We further explore the impact of XML on healthcare adoption and reliable diagnostic tool development. This chapter lays the groundwork for future research on model generalizability, ethics, and real-time integration. Adopting explainable models for liver disease diagnosis is crucial, and continuous innovation in XML techniques will meet evolving healthcare demands.