In the early stages, cirrhosis usually doesn’t cause symptoms. Only through routine blood tests or liver biopsy does a doctor diagnose damage to the liver. Using Machine learning we develop a model that can assist doctors in diagnosing the early stages of liver cirrhosis before it gets fatal. In this study, we use various machine learning algorithms to determine the liver cirrhosis stage. Data is collected from the Mayo Clinic trial, USA, in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The performance of the algorithms was evaluated using the ROC-AUC curve which is a very practical method of model evaluation for classification problems. Results showed that by applying Logistic Regression to predict the cirrhosis stage we get a ROC-AUC score of 0.74 which is considerable in view of the instance we have.