Liver Disease Prediction Using Ensemble Technique

Abstract

According to the World Health Organization (WHO), chronic illnesses account for over 59\% of global mortality, with liver diseases being a leading cause of death worldwide. Due to the liver’s ability to function even when partially damaged, liver issues often go undiagnosed until advanced stages. This paper presents a framework using clinical data and machine learning algorithms to predict liver disease. An ensemble approach processes data from liver patients and healthy donors through Gradient Boosting and AdaBoost classifiers. The model aims to identify high-risk individuals, enabling early detection and treatment, and highlights future integration with the broader healthcare industry.