Real-time Classification and Hepatitis B Detection with Evolutionary Data Mining Approach

Document Type : Original Article

Authors

1 Data Science Research Lab Blue Crest University, Monrovia, Liberia-1000

2 Sri Venkateswara University, Tirupati Andhra Pradesh, Tirupati District, India -517501

3 School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, 2050, South Africa

Abstract

Hepatitis is a disease that occurs in all ages and levels of the life of people. Hepatitis disease does not only have a deadly effect, but its identification, diagnosis, and early detection can help to treat the disease in the body and care and maintenance. Hepatitis has a variety of types that this type of study deals with hepatitis B. In this research, a new classification approach is developed for the diagnosis of hepatitis B disease using an optimized deep-learning method. This method, which involves the automatic extraction of features with minimum redundancy and minimum possible dimensions, and then modeling data from a low to a high level, can be used as a data mining method in the discovery and extraction of knowledge in computer-aided medical systems to be employed. Also, a series of evaluation criteria, including accuracy, to compare with the previous methods and to ensure the proposed approach is presented.

Keywords