Hepatitis C virus is a major cause for happening liver disease all over the world. However, many tools have been build that try to reduce the influence of this virus. In this work, a machine learning based model has been proposed that can classify hepatitis C virus infected patient's stages of liver. We gathered the instances of liver fibrosis disease of Egyptian patients from UCI machine learning repository. To balance instances of multiple categories, synthetic minority oversampling methodology has been used that increases synthetic instances of patients. Later, we applied different feature selection methods to identify significant features of hepatitis C virus in this dataset. Various classifiers has been employed to categorize patients into balanced primary, feature selected and primary HCV instances. After analyzing this results, KNN shows the best 94.40% accuracy than any other classifiers. This result has been useful to scrutinize and take decision in hepatitis C virus infectious disease.