An Optimal Pruning Fuzzy Learning Model for Analysing Risk Factors of Tuberculosis - Karabi Saikia (International Journal of Intelligent Systems and Applications in Engineering, 2022)
The most prevalent pathogens disease that causes death is tuberculosis (TB). Approximately 1,6 million per year or 4,384 people die every day of Tuberculosis globally, surpasses death number of HIV and Malaria combined according to WHO, 2018. Computational intelligence has the ability to tackle complex, broad, and ambiguous problems. However, existing computational techniques such as Fuzzy logic and Neural Network suffer from performance degradation when fitting the model to a guidance data set. We use computational techniques to analyse the risk factors of Tuberculos is on patient dataset publicly available in WHO portal. We apply pruning fuzzy model to reduce the network size while maintaining the fitting accuracy. To categorise input data, fuzzy C-means classifier and fuzzy inference system are utilised. To improve prediction accuracy, we use an Adaptive Neuro-fuzzy Inference System to generate fuzzy rules. The findings indicate that the suggested technique has greater precision, which meets the needs of the physicians. As a result, the created system will benefit both regular people and medical professionals.