NEURO-FUZZY APPROACH FOR DIAGNOSING AND CONTROL OF TUBERCULOSIS

Tuberculosis is the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. The main aim of this research work is to develop a Neuro-Fuzzy system for diagnosing tuberculosis. The system is structured with to accept symptoms with the help of three domain Medical expertise as inputs that are used to automatically generate rules that are injected in to the knowledge based where the system would use to make decisions and draw a conclusion. MATLAB 7.0 is used to implement this experiment using fuzzy logic and Neural Network toolbox. In this experiment linguistic variables are evaluated using Gaussian membership function. This system will offer potential assistance to medical practitioners and healthcare sector in making prompt decision during the diagnosis of tuberculosis. In this work basic emblematic approach using Neuro-fuzzy methodology is presented that describes a technique to forecast the existence of mycobacterium and provides support platform to researchers in the related field.