Hybrid RNN RBFN Model for Accurate Diabetes Prediction Using Evolutionary Gravitational Search and Dynamic Incremental Normalization

Diabetes is a chronic metabolic illness marked by high blood glucose levels, which can lead to serious complications if not recognized and treated promptly. Diabetes must be predicted accurately and early to allow for appropriate intervention and treatment planning. This study seeks to create a high performance predictive model for diabetes diagnosis by combining advanced pre processing, feature selection, and classification techniques.