Volume 6, Issue 2 (February 2019), Pages: 23-32
Original Research Paper
Title: Automated diagnosis of liver disorder using multilayer neuro-fuzzy
Author(s): Syeda Binish Zahra 1, 2, *, Atiffa Athar 3, Muhammad Adnan Khan 1, Sagheer Abbas 1, Gulzar Ahmad 1
1Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
2Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
3Department of Computer Science, CIIT, Lahore, Pakistan
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* Corresponding Author.
Corresponding author's ORCID profile: https://orcid.org/0000-0001-8852-691X
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For the last couple of decades, diagnosis is performed by expert physicians who spend a year or two to understand the complex phenomena of an organ disorder. Still, the diagnosis is not robust and there are errors due to tiredness or complexity of the disease. Our objectives are to propose an automated framework for a liver disorder which is based on the methodology of neural network techniques and fuzzy logic approaches. Abnormality of the liver is measured in a patient through a series of blood and function tests. The symptoms in a patient direct our proposed framework to suggest the procedure for the diagnosis whether it may be liver function test or liver blood test. The proposed intelligent system generates the level of abnormality based on outcomes of 4 different tests that form the input of the liver blood test in our fuzzy system. Our results show that the proposed system is not only accurate but it provides a baseline for automated systems that are robust and provide objectivity in the diagnosis and reduce the human error.
© 2019 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Fuzzy logic, Fuzzy rule based system, Fuzzy logic control system, Liver function test, Liver blood test
Article History: Received 5 September 2018, Received in revised form 14 December 2018, Accepted 15 December 2018
Compliance with ethical standards
Conflict of interest: The authors declare that they have no conflict of interest.
Zahra SB, Athar A, Khan MA et al. (2019). Automated diagnosis of liver disorder using multilayer neuro-fuzzy. International Journal of Advanced and Applied Sciences, 6(2): 23-32
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