Machine Learning Applications in Early Detection of Hepatic Steatosis Disease

Applications “of machine learning (ML) have become vital for the early identification and treatment of hepatic steatosis illness, which is a common liver condition characterized by increased buildup of fat in the liver without a significant amount of alcohol use. This innovative approach takes use of a variety of patient-specific data, including body mass index (BMI), height, weight, gender, and follow-up time (futime), to develop prediction models and algorithms. These characteristics are used to generate models and algorithms. Patient outcomes can be improved by healthcare professionals through proactive intervention, the customization of treatment programs, and the identification of patterns, correlations, and risk factors associated with non-alcoholic fatty liver disease (NAFLD) through the analysis of multidimensional datasets by machine learning algorithms. By using machine learning (ML) to the process of identifying nonalcoholic fatty liver disease (NAFLD), diagnostic accuracy is improved, and customized medicine techniques are advanced. This, in turn, leads to advancements in the treatment and early management of this chronic liver textbf” disease.