A Study on Liver Disease Using Different Machine Learning Algorithms

With a high death rate and a huge financial burden, liver disease is a serious global health problem. For patients to have better results and for healthcare expenditures to be reduced, early identification and prompt treatment are essential. Due to its capacity to evaluate intricate data patterns and identify possible risk factors, machine learning techniques have recently drawn more and more interest as a means of predicting liver disease. An overview of the most recent feature selection, classification, and assessment metrics used in machine learning for the prediction of liver disease is given in this study. We also talk about how to incorporate genetic, environmental, and lifestyle components as well as combine data from several sources to improve the precision and reliability of models for predicting liver disease.