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

Document Type : Original Article

Authors

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

2 Professor of IT & Head Research Data Science Research Lab BlueCrest University,Monrovia, Liberia-1000

3 Professor (SL), Sri Venkateswara University, Tirupati Andhrapradesh, Tirupati District, India – 517501

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.

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History

  • Receive Date: 01 February 2023
  • Revise Date: 27 February 2023
  • Accept Date: 13 March 2023

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