International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 7, No. 1, 8 Dec. 2014
Cover page and Table of Contents: PDF (size: 419KB)
A Mobile-Based Fuzzy System for Diagnosing Syphilis (Sexually Transmitted Disease)
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Author(s)
Index Terms
Fuzzy System, Mobile Based Fuzzy System, Membership Functions, Interval valued membership function, Root sum square, Diagnosis of Syphilis
Abstract
The high rate at which Africans die of syphilis yearly has been majorly attributed to the uneven ratio of the patients to competent medical practitioners who provide Medicare. This mortality rate has always drawn the attention of researchers and different approaches had been used to bring the rate down. This paper provides a software solution that personifies the expert-like way of providing diagnostic service to patients who suffer this disease. It is capable of making approximate diagnosis based on uncertainties. The system has been structured into five components: user interface, fuzzification, knowledge base, inference engine and defuzzification. The user interface uses a graphic user interface based method of human-computer interaction while the fuzzification component has transformed crisp quantities into fuzzy quantities using both interval-valued and S-curve membership functions. The reasoning has been achieved using root sum square (RSS) method and transformation of fuzzy values to scalar ones was through weighted average method. This system was tested and found effective.
Cite This Paper
Alaba T. Owoseni, Isaac O. Ogundahunsi, Seun Ayeni, "A Mobile-Based Fuzzy System for Diagnosing Syphilis (Sexually Transmitted Disease)", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.1, pp.33-40, 2015. DOI:10.5815/ijitcs.2015.01.04
Reference
[1]http://www.who.int/reproductivehealth/topics/rtis/treatment_syphilis.pdf, [retrieved on 3/14/2014].
[2]D. Aadland, D. Finnoff and X.D. Huang, “Syphilis Cycles”, 2012 retrieved from: http://www.uwyo.edu/ aadland/research/syphiliscycles.pdf on 26/03/2014.
[3]https://www.vdh.virginia.gov/epidemiology/factsheets/pdf/Syphilis.pdf, [retrieved on 3/14/2014].
[4]A. Ameri and H. Moshtaghi, “Design and development of an expert system in differential diagnosis of maxillofacial radio-lucent lesions”, retrieved from http://www.idt.mdh.se /kurser/ct3340/ archives/ht08/papersRM08/21.pdf on 15/3/2014
[5]L. S. Goggin, Robert H. Eikelboom, and Marcus D Atlas, “Clinical decision support systems and computer aided diagnosis in otology,” Otolaryngology-Head and Neck Surgery, 136:S21-S26, 2007.
[6]M. Z. Asghar, A. R. Khan, and M. J. Asghar, “computer assisted diagnoses for red eye (CADRE),” International Journal of Computer Science & Engineering, vol. 1(3), pp 163-170, 2009.
[7]H. H. Owaied, and M. M. Qasem, “Developing rule-case-based shell expert system,” Proceedings of International Multi Conference of Engineers and Scientists, 2010, retrieved from: http://www.iaeng.org/publication /IMECS 2010 _pp81-91.pdf on 15/3/2014.
[8]P. P. Tomar, and P. K. Saxena, “Architecture for medical diagnosis using rule-based technique,” First International Conference on Interdisciplinary Research & development, Thailand, vol. 25, pp. 1-25, 2011.
[9]M. H. F. Zarandi, M. Zolnoori, M. Moin, and H. Heidarnrjad, “A fuzzy rule based expert system for diagnosing asthma,” Industrial Engineering, vol. 17 (2), pp. 129-142, 2010.
[10]M. Patel, and P. Virparia, “Designing mobile based fuzzy expert system framework for viral infection diagnosis”, International Journal of Current Research and Review, vol. 4(12), pp. 139-146, 2012.
[11]S. S. Smita, S. Sikchi and M. S. Ali, “Generic medical fuzzy expert system for diagnosis of cardiac diseases,” International Journal of Computer Applications, vol. 66, no. 13, pp. 35-44, 2013.
[12]P. Aruna, N. Puviarasan and B. Palaniappan, “An investigation of neuro-fuzzy system in psychrosomatic disorders,” Expert Systems with Applications, vol. 28, pp 673-679, 2005.
[13]A. Banerjee, A.K. Majumdar and A. Basu, “A fuzzy expert system approach using multiple experts for follow-up of endemic diseases,” sadhana, India, vol. 19(1), pp. 51-73, 1992.