An Efficient Technique for Disease Diagnosis Using Bacterial Foraging Optimization and Artificial Neural Network

Abstract

Early diagnosis of any disease with less cost is always preferable. Diabetes is one such disease. It has become the fourth leading cause of death in developed countries and is also reaching epidemic proportions in many developing and newly industrialized nations. In this study, we investigate an automatic approach to diagnose Diabetes disease based on Bacterial Foraging Optimization and Artificial Neural Network The proposed BFO-ANN method obtains 94.68% accuracy on UCI diabetes dataset which is better than other models.