This research paper proposes a prediction system for liver disease using machine learning. Researchers provided various data to identify the causes for Hepatitis. Here, Decision tree method is used to determine the structural information of tissues. The algorithm used to construct the decision tree is C4.5 that concentrates on 19 attributes such as age, sex, steroids, antivirals, spleen, fatigue, malaise, anorexia, liver big, liver firm, spiders, vilirubin, varices, ascites, ALK phosphate, SGOT, albumin, protime, and histology for the diagnosis of the disease. These features helped in determining the abnormalities of the patient which resulted in 85.81% accuracy.