Adaptive Neuro Fuzzy Expert System for Diagnosing HIV

Authors(4) :-B. M. G. Amosa, S. P. Adisa, B. C. Ateko, J. I. Ugwu

Human Immunodeficiency Virus (HIV) is a retrovirus that causes Acquired Immune Deficiency Syndrome (AIDS) by infecting helper T cells or Lymphocyte of the immune system. HIV is transmitted primarily by exposure to contaminated body fluids, especially blood and semen. Other means of transmission of HIV include sharing contaminated sharp objects and blood transfusion. HIV symptoms can include a headache, chronic cough, diarrhea, swollen glands, lack of energy, and loss of appetite, weight loss, frequent fevers, frequent yeast infections, skin rashes, pelvic/abdominal cramps, sores on certain parts of your body and short-term memory loss. The focal point of this research is to describe and illustrate the application of fuzzy logic system to the diagnosis of HIV. It involves a sequence of methodological and analytical decision steps that enhance the quality and meaning of the logic produced. The system eliminates the uncertainties often associated with the analysis of HIV test data. To actualize the objective of this study, data collected from University Teaching Hospital, Ibadan, Nigeria were used as a set of parameters for diagnosis, and the software used for the development of necessary Graphical User interfaces (GUI) for fuzzy modeling using the fuzzy inference system editor, membership function editor, rule editor, rule editor, rule viewer and surface viewer was MATLAB, while MAMDANI editor GUI; a type of an Adaptive Neural Fuzzy Inference Systems was used for building and analyzing Mamdani

Authors and Affiliations

B. M. G. Amosa
Department of Computer Science, Federal Polytechnic, Ede Nigeria.
S. P. Adisa
ICT Center, Federal Polytechnic, Ede Nigeria.
B. C. Ateko
Department of Computer Science, Federal Polytechnic, Ede Nigeria.
J. I. Ugwu
Department of Computer Science, Federal Polytechnic, Ede Nigeria.

HIV Disease, Fuzzy Expert System, Fuzzy Logic, Medical Diagnosis.

  1. Christian Nordqvist (2018). Explaining HIV and AIDS.
  2. WHO (2018). HIV/AIDS.
  4. UNAIDS 2018 Fact sheet - Latest statistics on the status of the AIDS epidemic.
  5. Access Campaign (2010). The Ten Consequences of AIDS Treatment Delayed, Deferred, or Denied.
  7. M Negnevitsky, (2005) “Artificial intelligence: A guide to intelligent systems”, Addison Wesley Longman.
  8. Shi, Y. and Eberhart, R. and Chen, Y., (1999) “Implementation of evolutionary fuzzy systems”, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, pp. 109 - 119
  9. Pamela McCorduck (2004). Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. AK Peters Ltd .ISBN:1568812051
  10. Abraham, A. (2001) Neuro-Fuzzy Systems: State-of-the-Art Modeling Techniques, Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, in Lecture Notes in Computer Science, Vol. 2084, (eds. Mira., Jose and Prieto., Alberto) Springer Verlag, Germany. pp. 269 - 276.
  11. Microsoft Encarta (2018). Microsoft Encarta - latest version 2018 free download
  13. Donald, W.A. (1986) A Guide to Expert Systems, Addison- Wesley, Boston, MA. with eric horvitz 2006
  14. Wilcox, AB (July 2012). "Gallagher KD, Boden-Albala B, Bakken SR.". Med Care. doi:10.1097/MLR.0b013e318259c1e7. Retrieved 12 May 2017.
  15. Simon Kendal, Malcolm Creen (2007). An Introduction to Knowledge Engineering. Authors, Edition, illustrated. Publisher, Springer Science & Business Media, 2007. ISBN, 1846286670.
  16. Amosa Babalola, Hameed Aderemi, Kawonise Kayode, Ekuewa Jacob, (2017). Fuzzy Logic means for Intelligent Diagnosis of Obstetrics Fistula Disease. International Journal of Electrical, Electronics and Computers (ISSN: 2456-2319).2(6), pp. 01-06
  17. Wang, L.X. and Mendel, J.M. (1992) Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man, and Cybernetics, 22, pp. 1414-1427.
  19. DrIng. Habil. B. Möller. Uncertainty in Engineering, Fuzziness, 2005.
  20. URL:

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-05-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 254-265
Manuscript Number : CSEIT1833693
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

B. M. G. Amosa, S. P. Adisa, B. C. Ateko, J. I. Ugwu, "Adaptive Neuro Fuzzy Expert System for Diagnosing HIV", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.254-265, May-June-2018.
Journal URL :

Article Preview