A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis

Tuberculosis is a social, re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of tuberculosis disease (with HIV prevalence) in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for Tuberculosis diagnosis were been faced with a number of challenges which can, if measure not taken, increase the spread rate; hence, there is a need for aid in diagnosis of the disease. This study proposes a technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy Inferential method to provide a decision support platform that can assist medical practitioners in administering accurate, timely, and cost effective diagnosis of Tuberculosis. Performance evaluation observed, using a case study of 10 patients from St. Francis Catholic Hospital Okpara-In-Land (Delta State, Nigeria), shows sensitivity and accuracy results of 60% and 70% respectively which are within the acceptable range of predefined by domain experts.