International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.11, No.8, Aug. 2019

MeDevice: A Mobile – Based Diagnosis of Common Human Illnesses using Neuro – Fuzzy Expert System

Full Text (PDF, 561KB), PP.27-32

Views:3   Downloads:0


Johaira U. Lidasan, Martina P. Tagacay

Index Terms

Fever;Neuro-fuzzy expert;hybrid system;Gradient descent;optimization algorithm


Fever is a sign that the body is trying to fight infection. It is usually accompanied by various sicknesses or symptoms that signal another illness or disease. Diagnosing it ahead of time is essential because it has to do with human life and to determine what to do to get well. MeDevice is a mobile-based application that runs in Android devices that allows the user to enter the levels of his/her symptoms and diagnoses the disease either as influenza, dengue, chicken pox, malaria, typhoid fever, measles, Hepatitis A and pneumonia together with its details and its first aid treatment. It aims at providing an efficient decision support platform to aid people with fever in diagnosing their disease and whether or not to seek medical attention especially in developing countries like the Philippines. This application is engineered with the knowledge base and the inference method of fuzzy logic and expert system with the help of Gradient Descent optimization algorithm and back propagation neural network to achieve the optimum value of the error rate. This is essential to provide the application with a high accuracy rate which shows during the conduct of testing of the application.

Cite This Paper

Johaira U. Lidasan, Martina P. Tagacay, "MeDevice: A Mobile – Based Diagnosis of Common Human Illnesses using Neuro – Fuzzy Expert System", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.8, pp.27-32, 2019. DOI: 10.5815/ijitcs.2019.08.04


[1]D. Ogoina, “Fever, fever patterns and diseases called ‘Fever’ – A Review” J Infect Public Health, vol 4(3), pp. 108-24, August 2011, doi: 10.1016/j.jiph.2011.05.002. 

[2]J. Taber, Ph.D., B. Leyva, B.A. and A. Persoskie, A, “Why do People Avoid Medical Care? A Qualitative Study using National Data”, J Gen Intern Med, vol 30(3), pp. 290-297, March 2015, doi: 10.1007/s11606-014-3089-1

[3]V. Makoge, H. Maat, L. Vaandrager and M. Koelen, “Health-Seeking Behaviour towards Poverty-Related Disease (PRDs): A Qualitative Study of People Living in Camps and on Campuses in Cameroon”, PLoS Negl Trop Dis, Vol 11(1). January 2017, doi: 10.1371/journal.pntd.0005218

[4]Tutorials Point, “Artificial Intelligence” [Online], 2015 Available:

[5]M. Hasan, K. Sher-E-Alam and A. Chowdhury, “Human Disease Diagnosis using a Fuzzy Expert System”,  Journal of Computing, vol 2(6), June 2010, pp. 66

[6]S. Shwartz and S. David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014

[7]Department of Health, Philippine Health Advisories, National Center for Health Promotion, 2012

[8]I.B. Ajenaghughurure and P. Sujatha Ph.D., “Fuzzy Based Multi-Fever Symptom Classifier Diagnosis Model”, I. J. Information Technology and Computer Science, vol 10(13), 2017, pp. 13-28, doi: 10.5815/ijitcs.2017.10.02

[9]S. Tunmibi, O. Adeniji, A. Aregbesola and A. Dasylva, “A Rule Based Expert System for Diagnosis of Fever”,  Int J Adv Res, vol 1(7), September 2017, pp. 343-348

[10]O. W. Samuel and M.O. Omisorre, “Hybrid Intelligent System for the Diagnosis of Typhoid Fever”, J Comput Eng Inf Technol, vol2(2), August 2013, doi: 10.4172/2324-9307.1000109

[11]M.G. Asogbon, O.W. Samuel, M.O. Omisorre and O. Awonusi, “Enhanced Neuro-Fuzzy System Based on Genetic Algorithm for Medical Diagnosis”, J Med Diagn Methods, vol 5(1), February 2016, doi: 10.4172/2168-9784.1000205

[12]T. Faisal, M.N. Taib and F. Ibrahim, “Adaptive Neuro-Fuzzy Inference System for Diagnosis Risk in Dengue Patients”, Expert Syst Appl, vol 39(4), March 2012, pp. 4483-4495, doi:

[13]A. Owoseni and I. Ogundahunsi, “Mobile-Based Fuzzy Expert System for Diagnosing Malaria”, I J Information Engineering and Electronnic Business, vol 2, March 2016, pp. 14-22, doi: 10.5815/ijieeb.2016.02.02

[14]L.A. Zadeh, “Fuzzy Sets versus Probability”, Proceeding of the IEEE. Vol 68(3). March 1980, pp. 421 – 421. Doi: 10.1109/PROC.1980.11659

[15]T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control”, IEEE Trans Cybern, Vol SMC-15(1), January 1985, Doi: 10.1109/TSMC.1985.6313399

[16]M. Blej, and M. Azizi, “Comparison of Mamdani – Type and Sugeno – Type Fuzzy Inference Systems for Fuzzy Real Time Scheduling”, Int J Appl Eng Res, vol 11(22), 2016, pp. 11071-11075

[17]A. Lotfi, “The Importance of Learning in Fuzzy Systems”, European Society for Fuzzy Logic and Technology. (Online) Available: