An ANFIS-PSO Algorithm for Predicting Four Grades of Non-Alcoholic Fatty Liver Disease

Non-Alcoholic Fatty Liver Disease is a kind of chronic disease which rigorous prediction is quite difficult at early stages. The prediction of fatty liver plays significant role in treating the disease and also constraining the next health consequences. In this paper, an approach has been taken to predict liver grades and what affects its severity. The evaluation of relation between the blood test features and visual analysis based on ultrasound images has been done by effective cooperation of Iranian liver specialists. In this study the dataset of 400 liver patients with seven vital features is used in determining the performance of adaptive neuro fuzzy inference system method integrating with particle swarm optimization with measurement of Root Mean Square Error (RMSE) as a factor to assess the accuracy of model.