Genetic-Neuro-Fuzzy Inferential Model for Tuberculosis Detection

摘要

Tuberculosis is one of the dangerous infectious diseases that can be characterized by the growth of tubercles in the tissues. This disease mainly affects the lungs and also the other parts of our body. 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 practitioner in administering accurate, timely, and cost effective diagnosis of Tuberculosis. The medical record of 100 TB patients aged 15 to 75 were used to evaluate the performance of the multi-technique decision support system. 70% of the dataset was used training data, 15% was used for validation while the remaining 15% was used to observe the performance of the proposed system.