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Research Article Open access CC BY 4.0

Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

Ibrahim Goni, Christopher U. Ngene, Manga I., Auwal Nata’ala, Sunday J. Calvin

Asian Journal of Research in Computer Science · pp. 1–9 · Published 25 Oct 2018

10.9734/ajrcos/2018/v2i124763

Abstract

Tuberculosis is a contiguous disease that is causing death both in developed and developing countries. The main aim of this research work was to a developed an intelligent system for diagnosing Tuberculosis using adaptive neuro-fuzzy methodology. Eleven symptoms of tuberculosis which are persistent cough for more than two weeks, cough with blood, weight loss, tiredness, chest pain, fever, difficulty in breathing, loss of appetite, lymph node enlargement, history of TB contact and night Sweat are assigned with weights which are categorize best on severity level as mild, moderate, severe and very severe, yes and no which serve as inputs to the adaptive neuro-fuzzy inference system (ANFIS). MATLAB 7.0 is used to implement this experiment, Trapezoidal Membership function was used, back propagation algorithm was used for training and testing, the error obtain is 0.41777 at epoch 2 which shows that the training performance is exactly 99.58223 and testing performance of the system are 99.58197 at epoch 2.   

Adaptive neuro-fuzzy tuberculosis Gaussian membership function hybrid training algorithm epoch

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