Hybrid of particle swarm optimization algorithm and fuzzy system for diabetes diagnosis.

Diabetes is a dangerous disease in which the body is incapable of controlling blood sugar due to inadequate insulin hormone levels. This chronic disease increases blood sugar in patients. Therefore, if it is not controlled, it will cause many complications. A considerable number of people in the world suffer from this disease owing to its damage and lack of its initial diagnosis. The patient visits the doctor frequently to diagnose his/her illness and conducts various tests that are boring and costly. Increasing machine learning approaches through heuristics, and novel methods can somewhat decrease the problems. The current study aims to propose a model that can predict diabetes in patients with high accuracy. The paper introduces a new method based on the assortment of metaheuristic algorithms of a particle swarm and fuzzy inference system. The proposed method utilizes fuzzy systems to binary the particle swarm algorithm. The achieved model is applied to the diabetes dataset and then evaluated using a neural network classifier. The results indicate an increase in classification accuracy to 95.47% compared to other existing methods. [ABSTRACT FROM AUTHOR]