Analysis of Chronic Liver Disease Detection by Using Machine Learning Techniques

A significant cause of illness and death across the globe, chronic liver disease affects millions of people every year. Improving patient outcomes and decreasing the strain on healthcare systems depend on early identification and management. Over the last several years, advances in machine learning technology have shown great promise in helping physicians detect chronic liver disease in its earliest stages. This review aims to assess how well machine learning methods work and where they fall short in identifying the early stages of chronic liver disease. This study assesses the present status of research through a review of the relevant literature, experimental investigations, and clinical applications. The research critically assesses the efficacy of machine learning models regarding precision, recall, and transfer. The paper explores how aspects such as sample size, feature selection, and model complexity affect the efficiency of such methods.