Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). In 2015, WHO reports that 71 million people were living with HCV, and 1.34 million died. In 2017, 13.1 million infected people knew their diagnosis and around 5 million patients were treated. HCV can cause acute and chronic hepatitis, where 20% of chronic hepatitis progresses to final-stage chronic liver cancer. Currently, no vaccine of HCV exists, and no effective treatments are available for demolishing the progression of hepatitis C. So spotting the stages of the disease is essential for diagnostic and therapeutic management of infected patients. This paper attempts to detect stages of hepatitis C virus so that further diagnosis and medication of hepatitis patients can be prescribed. It uses a supervised artificial neural network to make a prediction. Evaluation of results is done by cross-validation using the holdout method. Hepatitis C Egyptian-patients' dataset from UCI Machine Learning Repository is used for feeding the algorithms. The research succeeds to detect the hepatitis C stages and achieves an accuracy of 97%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.