Classification Model for Hepatitis B Disease Using Supervised Machine Learning Technique

Abstract

Hepatitis B is the most common serious liver infection in the world and caused by the Hepatitis disease. This results in many people injure and deaths, many human life lost due to this disease. The Most countries around the world, including Ethiopia, have increased the number of patients. This has led to an increase in the number of life lose. However, it is frequently challenging to determine which specific environments lead to such factor. Various studies have been conducted to classify hepatitis B disease, and others are focusing on whether the peoples will live or die because of this disease. Furthermore, most of the studies conducted so far focused on hepatitis B disease prediction with fewer number of features. The study aims to classify the factors relevant to hepatitis B disease such as chronic and acute hepatitis B disease factors based on the independent variables collected from Arba Minch. The data for this study was collected from Arba Minch General Hospital. It covers ten years hepatitis B patient data record from the year 2002-2012 E.C. the preprocessed dataset has 14 attribute and 50032 instance. This study has been conducted using an experimental approach to determine the best- performing model. This study used the WEKA tool and Asp.Net programming language for implementation and analysis purposes. For this study, the researchers trained four different models, including J48, REP Tree, Bayes Net, and PART algorithms. Those models are selected based on a comprehensive study showed to select the best First style performing model. In this study, evaluation of the model was done using percentage split (80/20), and classification performance metrics was used in order to compare the models. The finding of this study displays that the J48 classifier outclasses then the rest of the classifiers with an accuracy of 85.5% on training data and 82.7% on test data. Based on this result, a system prototype was developed and tested that is accomplished of classifying features of hepatitis B disease. Keywords: Machine Learning, Classification Algorithm, J48, Hepatitis B Diseases DOI: 10.7176/CEIS/13-3-01 Publication date: May 31 st 2022