Predicting the Existence of Mycobacterium Tuberculosis Using Hybrid Neuro Adaptive System

Navneet Walia*, Harsukhpreet Singh**, Anurag Sharma***
* PG Scholar, Department of Electronics and Communication Engineering, CT Institute of Technology and Research, Jalandhar, India.
**-*** Assistant Professor, Department of Electronics and Communication Engineering, CT Institute of Technology and Research, Jalandhar, India.
Periodicity:September - November'2015
DOI : https://doi.org/10.26634/jcom.3.3.3663

Abstract

This paper introduces a systematic approach for design of fuzzy inference system based on the class of neural network to predict the existence of Mycobacterium tuberculosis. Fuzzy systems have reached a recognized success in several applications to solve diverse class of problems. Currently, there is an existence trend to expand them in medical field and using them with adaptation capabilities through combination with other various techniques. This article focus on the development of data mining solution using Adaptive Neuro Fuzzy Inference System (ANFIS) that makes diagnosis of tuberculosis bacteria as precise as possible and helps in deciding whether it is reasonable to start treatment without waiting for the accurate medical tests. Dataset are collected from 200 different patient records which are obtained from health clinic (consent of physicians and patients). Patient record has 19 different input attributes which covers demographic and medical test data. The transparency, objectivity and easy implementation of the proposed method generates classes of tuberculosis that suits the need of pulmonary physicians and decrease the time consumed in generating diagnosis provide a useful way to start diagnosis in more reasonable and fairer manner.

Keywords

Artificial Intelligence, ANFIS, Expert System, Fuzzy Logic, Tuberculosis.

How to Cite this Article?

Walia, N., Singh, H., and Sharma, A. (2015). Predicting the Existence of Mycobacterium Tuberculosis Using Hybrid Neuro Adaptive System. i-manager’s Journal on Computer Science, 3(3), 28-40. https://doi.org/10.26634/jcom.3.3.3663

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