A Comparative Study on Prediction of Hepatitis B Virus (HBV) Using Machine Learning Algorithm

Abstract

The Hepatitis B virus (HBV) can cause acute and chronic liver infections. It is transmitted through infected blood products, unprotected sex, infected items such as needles, razor blades, dental or medical equipment, unscreened blood transfusions, or from mother to child at birth. Despite a considerable body of published research on hepatitis B in Bangladesh, researchers continue to lament the lack of reliable information about hepatitis B virus (HBV) infection epidemiology. The present review aims to provide a comprehensive survey of the literature with particular focus on a number of epidemiological questions, as well as a commentary on the trends of hepatitis B research as it has taken place in Bangladesh. it is difficult to provide conclusive estimates about HBV prevalence in the general population of Bangladesh. So, the aim of this piece of research work is to analysis the symptoms of Hepatitis B and Hepatitis C fever and early prediction of the symptoms that can be seen in years ahead. For predicting the symptoms two different Machine Learning algorithms have used. Support vector machine (SVM) and random forest classifier algorithm have been used. Finally, the accurate of these two has been evacuated and the confusion matrix has been shown and then we have talked about the algorithm which is better for our dataset.