INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.8, No.2, Mar. 2016

Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)

Full Text (PDF, 640KB), PP.14-22


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Author(s)

Alaba T. Owoseni, Isaac O. Ogundahunsi

Index Terms

Mobile Fuzzy System;Malaria Diagnosis;Expert System;Interval-valued Fuzzy Set;Triangular Membership Function;Membership Function

Abstract

Malaria is a deadly disease that claims yearly lives of millions in Africa, and other endemic continents. The prevalence of malaria in these endemic regions is majorly attached to the lack of competent medical experts who are capable of providing medical care for the affected victims. This study considers developing a mobile based fuzzy expert system that could assist in diagnosing malaria. The fuzzification of crisp inputs by the system was carried out using an inter-valued and triangular membership functions while the deffuzification of the inference engine outputs was performed by weighted average method. Root sum square method of drawing inferences has been employed while the whole development has been achieved with the help of Java 2 Micro Edition of Java. This expert system executes on the readily available mobile devices of the patients. This fuzzy system was finally evaluated and confirmed effective in providing a human-expert like diagnosis.

Cite This Paper

Alaba T. Owoseni, Isaac O. Ogundahunsi,"Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.2, pp.14-22, 2016. DOI: 10.5815/ijieeb.2016.02.02

Reference

[1]United State Department of Health and Human Services, "Understanding Malaria: Fighting an Ancient Scourge (NIH Publication No. 07-7139) 2007", National Institutes of Health, retrieved on 22/05/2015, from http://www.niaid.nih.gov/topics/malaria/documents/malaria.pdf.

[2]S. Tunmibi, O. Adeniji, A. Aregbesola, and A. Dasylva, "A Rule Based Expert System for Diagnosis of Fever", International Journal of Advanced Research, 2013, 1(7), pp. 343-348.

[3]A. O. Adetunmbi, A. Oguntimilehin, and S.O. Falaki, "Web-Based Medical Assistant System for Malaria Diagnosis and Therapy", GESJ: Computer Science and Telecommunications, 2012, 1(33), pp. 42-53.

[4]World Health Organization, "Guidelines for the treatment of malaria", 2nd Edition, retrieved on 06/01/2011, from http://whqlibdoc.who.int/ publications/2010.

[5]F. M. E. Uzoka, O. U. Obot and K. Barker, "A performance comparison of Fuzzy Logic and AHP as engines for the development of Intelligent Medical Diagnosis Systems" Nigeria Computer Society 23rd National Conference, 2009, pp. 135- 150.

[6]I. Kamukama, "Clinical protocol-based decision support system for malaria treatment", M.Sc. Computer Science Thesis, Makerere University, 2009, retrieved on 22/05/2015, from http://hdl.handle.net/10570/463 /123456789/628/3/kamukama-ismail-cit-mastersreport.pdf.

[7]A. B. Adehor, and P. R. Burrell, "The Integrated management of health care strategies and differential diagnosis by expert system technology: A single-dimensional approach", World Academy of Science, Engineering and Technology, 2008, 44, pp. 533-538.

[8]S. O. Anigbogu, "Artificial intelligence-based medical diagnostic expert system for malaria and the related ailments", International Journal of Computer Science & Applications, 2006, 12(1), retrieved on 06/12/2013, fromhttp://www.researchgate.net/publication/240702829_Artificial_Intelligence-Based_Medical_Diagnostic_Expert_ System_For_Malaria_And_The_Related_Ailments.

[9]M. E. Rafael, T. Rerrie, A. Magill, Y. Lim, F. Girosi, and R. Allan, "Reducing the burden of childhood malaria in Africa: the role of improved diagnostics", Nature Publishing Group, pp. 39-48, retrieved on 06/12/2013 from http://www.nature.com/nature/journal/v444/n1s/full /nature05445.html

[10]World Health Organization, "Guidelines for the treatment of malaria", Second Edition, retrieved on 22/05/2015, from http://whqlibdoc.who.int/1207966204/9789241547925_eng.pdf.

[11]D. Chandramohan, I. Carneiro, A. Kavishwar, R. Brugha, V. Desai, and B. A. Greenwood, "Clinical algorithm for the diagnosis of malaria: results of an evaluation in an area of low endemicity", Tropical Medicine and International Health, 2001, 6(7), pp. 505-510.

[12]K. A. Bojang, S. Obaro, L. A. Morison, and B. M. Greenwood, "A prospective evaluation of a clinical algorithm for diagnosis of malaria in Gambian Children", Tropical Medicine and International Health, 5(4), pp. 231-236. 

[13]M. Patel, and P. Virparia, "Designing Mobile Based Fuzzy Expert System Framework for Viral Infection Diagnosis", International Journal of Current Research and Review, 2012, 4 (12), pp. 139-146.

[14]M. O. A. Olufemi, "Mobile Phone-Based Expert System for Disease Diagnosis", retrieved on 22/05/2015, from http://www.irma-international.org/412224158/A-Mobile-Phone-Based-Expert-System-for-Disease-Diagnosis.pdf.

[15]M. Scarpiniti, "Neural Networks Lesson 9- Fuzzy Logic", 2009, retrieved on 22/05/2015, from http://ispac.ing.uniroma1.it/scarpiniti/files/NNs/Less9.pdf

[16]P. P. Costas and I. S. Constantinos, "Chapter 15: Fuzzy Reasoning", retrieved on 22/05/2015, from http://www.inf.ufpr.br/1420547959/FR.pdf.