JCOM_V3_N3_RP5 Navneet Walia Harsukhpreet Singh Anurag Sharma Journal on Computer Science 2347–6141 3 3 28 40 Artificial Intelligence, ANFIS, Expert System, Fuzzy Logic, Tuberculosis 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. September - November 2015 Copyright © 2015 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=3663