ML-Driven Solutions for Chronic Liver Disease: Predictive Models and Prevention Strategies

Abstract

Chronic liver disease (CLD) is a major global health issue, often progressing silently until it reaches advanced stages, leading to severe illness and death if not detected early. The liver plays a critical role in filtering toxins and aiding digestion, so any disruption can have serious consequences. Raising awareness about liver health is crucial because early detection can change the disease’s course, opening doors to treatment and even reversing damage in some cases. In the United States, CLD has become a growing health crisis, with increasing deaths linked to conditions like hepatitis C, alcohol-related liver disease, and nonalcoholic fatty liver disease (NAFLD), largely due to rising obesity rates. These statistics highlight the urgent need for improved prevention, early diagnosis, and treatment strategies. Advances in machine learning (ML) offer new hope in fighting liver disease. Predictive models like RandomForest, LightGBM, XGBoost, Logistic Regression, Ensemble Learning Stacking, and ExtraTrees can accurately forecast the onset and progression of liver disease. These models enable earlier identification of at-risk individuals and more effective interventions, potentially transforming how we manage CLD. By leveraging these technological innovations, we can significantly reduce mortality rates and empower healthcare providers to take swift, decisive action in preventing and treating liver diseases.