Recognition of diabetes disease using a new hybrid learning algorithm for NEFCLASS

Classification systems have been widely applied in different fields such as medical diagnosis. Interpretability represents the most important driving force behind the implementation of fuzzy-based classifiers for medical application problems. Neuro-fuzzy classification approaches aim at creating fuzzy classification rules from data. The simplest model is The NEFCLASS; it is able to learn fuzzy rules and fuzzy sets by simple heuristics. In this paper we present a new hybrid learning algorithm for this model using Particle Swarm Optimization PSO for adjusting membership functions parameters. Experiments are performed on the Pima Indian Diabetes dataset available in UCI machine learning repository. The results indicate that the proposed method can work effectively for classifying the diabetes with an acceptable accuracy and transparency.