Machine Learning Algorithm Applied for Predicting the Presence of Mycobacterium Tuberculosis
International Journal of Clinical Dermatology
Volume 3, Issue 1, June 2020, Pages: 4-7
Received: Nov. 17, 2019;
Accepted: Dec. 20, 2019;
Published: Jan. 10, 2020
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Ibrahim Goni, Department of Computer Science, Faculty of Science, Adamawa State University, Mubi, Nigeria
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Tuberculosis is a contagious disease that causes of death. The body's response to active TB infection produces inflammation that can damage the lungs. Areas affected by active TB gradually fill with scar tissue. It is spread from person-to-person. A person is often infected by inhaling the germs. Tuberculosis germs are spread into the air when a person with TB disease of the lungs or throat coughs, sneezes, speaks, or sings. These germs can stay in the air for several hours, depending on the environment. However, patients with chronic diseases, such as diabetes, chronic kidney disease, and silicosis, are at elevated risk. Finally, age younger than 4 years, long-term malnutrition, and substance abuse are independent risk factors for disease. The aim of this research work is to develop an adaptive Neuro-Fuzzy system for predicting the presence of Mycobacterium tuberculosis. The system is structured with inputs and one output of which rules were generated by the system with the help of three domain Medical expertise and are injected in to the knowledge based where the system would use this rules to make decisions and draw a conclusion. MATLAB7.0 was used to implement this experiment using fuzzy logic and Neural Network toolbox. In this experiment linguistic variables are evaluated using Gaussian membership function.
Mycobacterium, Tuberculosis, Adaptive Neuro-fuzzy, MATLAB
To cite this article
Machine Learning Algorithm Applied for Predicting the Presence of Mycobacterium Tuberculosis, International Journal of Clinical Dermatology.
Vol. 3, No. 1,
2020, pp. 4-7.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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Michael N. (2005). Artificial Intelligence A Guide to Intelligent Systems 2nd Edition Addison wisely Pearson Education Limited England.
Mahmoud R. S. et al (2015). 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 Springer DOI 10.1007/s11517-015-1323-6.
James M., (2017). Tuberculosis: Causes, Symptoms, and Treatments MNT available online at http://www.medicalnewstoday.com/articles/8856.php [accessed January, 2017].
Karahoca A., & D. Karahoca, (2013). Tuberculosis disease diagnosis by using adaptive Neuro- fuzzy inference system and rough sets, pp. 471–483.
Cinetha K. and Uma P. M. (2014). Decision Support System for Precluding Coronary Heart Disease (CHD) Using Fuzzy Logic International Journal of Computer Science Trends and Technology (IJCST) – Vol. 2 (2) pp. 102-107.
Awotunde J. B., Matiluko O. E., & Fatai O. W (2014). Medical Diagnosis system using fuzzy logic African Journal of Computing and ICT Vol. 7 (2) pp. 99-106.
Neshat M., and Yaghobi M., (2009). “Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System”, in World Congress on Engineering and Computer Science (WCECS), 2009, Vol. II.
Varinder P., (2015). “Fuzzy Expert System for Medical Diagnosis “InternationalJournal of Scientific and Research Publications, Vol. 5 (1) ISSN 2250-3153.
Ekong et al. (2013) A fuzzy inference system for predicting depression risk levels African Journal of mathematics and computer Science Research Vol. 6 (10) pp. 197-204.
Putu M. P. and Ketut D. P. (2012). Fuzzy Knowledge-based System with Uncertainty for Tropical Infectious Disease Diagnosis International Journal of Computer Science Vol. 9 (3) pp 157-163 [online] available online at http://www.ijcsi.org/papers/IJCSI-9-4-3-157-163.pdf.