An Improved Asynchronous Tuberculosis Diagnosis System using Fuzzy Logic Mining Techniques
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2018 by IJCTT Journal | ||
| Volume-59 Number-1 |
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| Year of Publication : 2018 | ||
| Authors : Morgan O. Obi , Eke B.O, Asagba P.O | ||
| DOI : 10.14445/22312803/IJCTT-V59P104 |
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) V59(1):20-25, May 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
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.
Reference
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Keywords
Defuzzification, Fuzzification, Inference System, Linguistic Variables, Matlab, Membership Function.