Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System. (5th February 2019)

• Record Type: Journal Article
• Authors: Ahmad, Gulzar; Khan, Muhammad Adnan; Abbas, Sagheer; Athar, Atifa; Khan, Bilal Shoaib; Aslam, Muhammad Shoukat
• Other Names: Rizzuto Emanuele Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2019(2019)
• Publication Date: 2019-02-05
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2019/6361318
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems. The further input variables at layer II are HBsAg, anti-HBsAg, anti-HBcAg, anti-HBcAg-IgM, HBeAg, anti-HBeAg, and HBV-DNA that determine the output condition of hepatitis such as no hepatitis, acute hepatitis, or chronic hepatitis and other reasons that arise due to enzyme vaccination or due to previous hepatitis infection. This paper presents an analysis of the results accurately using the proposed ADHB-ML-MFIS expert system to model the complex hepatitis B processes with the medical expert opinion that is collected from the Pathology Department of Shalamar Hospital, Lahore, Pakistan. The overall accuracy of the proposed ADHB-ML-MFIS expert system is 92.2%.