An Improved Asynchronous Tuberculosis Diagnosis System using Fuzzy Logic Mining Techniques

Morgan O. Obi; Eke B.O; Asagba P.O

doi:https://doi.org/10.14445/22312803/IJCTT-V59P104

Research Article | Open Access | Download PDF

Volume 59 | Number 1 | Year 2018 | Article Id. IJCTT-V59P104 | DOI : https://doi.org/10.14445/22312803/IJCTT-V59P104

An Improved Asynchronous Tuberculosis Diagnosis System using Fuzzy Logic Mining Techniques

Morgan O. Obi , Eke B.O, Asagba P.O

Citation :

Morgan O. Obi , Eke B.O, Asagba P.O, "An Improved Asynchronous Tuberculosis Diagnosis System using Fuzzy Logic Mining Techniques," International Journal of Computer Trends and Technology (IJCTT), vol. 59, no. 1, pp. 20-25, 2018. Crossref, https://doi.org/10.14445/22312803/IJCTT-V59P104

Abstract

Tuberculosis is an air borne sickness that could easily be transmitted through numerous mediums like sneezing, coughing, making a song, speaking and so forth. It results from a bacterium named Mycobacterium tuberculosis. Improper diagnosis of this disease can lead to increased fatality and further spread of the disease. This work tends to proffer a diagnostic system that will aid in fast and accurate diagnosis of this disease which will aid in early treatment and isolation of carrier to curtail further spread of the disease which according to World Health Organization (WHO), kills over 4,000 people each day. The proponents made use of Fuzzy Logic Mining Techniques to model uncertainty inherent in diagnosis and implement the system making use of asynchronous techniques which improves the performance of the system and produce results of diagnosis without delay.

Keywords

Defuzzification, Fuzzification, Inference System, Linguistic Variables, Matlab, Membership Function.

References

[1] WHO. (2016). Chest Radiography in Tuberculosis Detection. Switzerland: Knut Lönnroth.
[2] Gerald, L. M., John, E. B., & Raphael. (2010). Mandell, Douglas, and Bennett`s principles and practice of infectious diseases. Philadelphia:Churchill Livingstone/Elsevier.
[3] Mahmoud, R. S., Shahaboddin, S., Shahram, G., Teh, Y. W., Aghabozorgi, S., Laiha, M. K., et al. (2014). RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system. International Federation for Medical and Biological Engineering.
[4] Djam, X.Y. and Y.H. Kimbi. (2011), A Decision Support System for Tuberculosis Diagnosis. Pacific Journal of Science and Technology. 12(2):410-425.
[5] Olabiyisi,S. O, Omidiora, E. O, Olaniyan, M. O & Derikoma, O. A Decision Support System Model for Diagnosing Tropical Diseases Using Fuzzy Logic. Afr J. of Comp & ICT. Vol 4, No. 2. Issue 2: 1-6 (2011).
[6] Imianvan, A. A and Obi J, C (2012). Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means. International Journal of Artificial Intelligence & Applications (IJAIA), 3(1).
[7] Onuwa, B. O. (2014). Fuzzy Expert System for Malaria Diagnosis. Oriental Journal of Computer Science and Technology , 7(2), 273 - 284.
[8] Al-Aidaroos, K. M., Bakar, A. A., & Othman, Z. (2012). Medical Data Classification with Naïve Bayes Approach. International Technology Journal , 1166- 1174.