A survey on data mining and machine learning techniques for diagnosing hepatitis disease

With the advancement of technology in recent years, different new techniques are being used for classification and prediction of different complex diseases, as well as to analyse biomedical data in the medical field. Hepatitis is a liver disease that has an adverse influence on people of any age group and generally, no symptoms appear. Hence, the diagnosis of hepatitis in the early stage becomes crucial. Use of technology can ease the process and so researchers have proposed some classification techniques for early detection of hepatitis. This paper aims at summarising the up-to-the-minute techniques used for the diagnosis and prediction of hepatitis and in order to fulfil the goal, numerous articles from 1996 to 2020 have been investigated. Through this work, the mostly used algorithms and their efficiency have come to light. It is evident that support vector machine (SVM), artificial neural network (ANN) and fuzzy methods are top three techniques that have been used by the researchers and provided good performance. This research work can be helpful to develop new techniques in future by knowing the pitfalls of the previous ones.