Paper Key : IRJ************891
Author: Asma Khatoon,Dr Raisul Hasan
Date Published: 05 Jan 2025
Abstract
Abstract Influenced by lifestyle factors such as excessive alcohol consumption, exposure to environmental toxins, consumption of contaminated or processed foods, and excessive misuse of prescription drugs, liver diseases are rapidly becoming a major health burden worldwide. Additionally, conditions such as viral hepatitis, obesity, and metabolic syndrome are major contributors to the increasing prevalence of liver disease. The liver plays a central role in metabolic processes, detoxification, and nutrient storage, making its dysfunction a major concern for overall health. Chronic liver disease (CLD) has become a widespread problem, affecting populations in both developed and developing countries, with a worrying trend being that it is being diagnosed more often in younger individuals, often in their mid-20s. This demographic shift highlights the urgent need for strong public health strategies, early detection tools, and preventive care to reduce its impact.This paper presents a thorough review of the prominent ML applications developed for diagnosing and managing liver disease. It highlights integrating data mining techniques to enhance diagnostic accuracy and provide early intervention support for medical practitioners. Additionally, a comparative analysis of selected studies is performed to assess their methodology, results, and its practical deductions. This comparative study results are intended to contributing in the development of more efficient, reliable, and accessible diagnostic tools, ultimately helps in improving patient outcomes and addressing the growing burden of liver diseases.