Tuberculosis Diagnosis Using Adaptive NeuroFuzzy Inference Systems

Authors

  • Semagn Tiruneh Sisay
  • Niketa Gandhi

Keywords:

artificial intelligence, adaptive neuro-fuzzy inference systems, diagnosis, expert systems, fuzzy logic, neural network, tuberculosis

Abstract

Tuberculosis (TB) is one of the leading infectious diseases all over the world. TB affects millions of people every year and more than 10% of them die due to this disease. Despite the belief that it is almost under control and the availability of age-old cure effective available, TB continues to infect humankind and it remains a global emergency. The traditional methods of TB diagnosis are inaccurate and timetaking, expensive, low efficacy rates, may give false results, cannot differentiate between latent TB and active TB, and unable to differentiate drug resistant TB stages, and cannot be detect TB in case of HIV and TB co-infection due to low levels of TB bacteria. Besides, TB diagnosis in developing countries faced challenges like poor diagnosis tools, low level laboratory systems and medical facilities, and lack of data processing culture. Therefore, it is inevitable to search for new TB diagnosis techniques that give accurate results with a greater speed. This study proposes a technique for TB diagnosis using Adaptive Neuro-Fuzzy Inferential System to provide a tool for accurate, timely, and cost effective diagnosis of Tuberculosis.

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Published

2017-07-01

How to Cite

Semagn Tiruneh Sisay, & Niketa Gandhi. (2017). Tuberculosis Diagnosis Using Adaptive NeuroFuzzy Inference Systems. Journal of Network and Innovative Computing, 5, 6. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/135

Issue

Vol. 5 (2017)

Section

Original Article

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