Optimized Deep Learning for Diabetes Detection: A BGRU-based Approach with SA-GSO Hyperparameter Tuning

Every year, millions of people are impacted by diabetes, a metabolic disease. A lower quality of life and an increased risk of critical organ failure are both linked to it. Diabetes management relies on early detection and consistent monitoring. Modern technological advancements in remote patient monitoring have the potential to revolutionise intervention and therapy paradigms. Using the PIMA Indian Diabetes dataset, this work applies machine learning approaches to recover the prediction accuracy of diabetes diagnosis. The dataset was subjected to thorough preparation, which involved imputation of Multivariate Imputation by Chained Equations (MICE) technique and feature scaling standardisation. The most important attributes leading to diabetes prediction were efficiently discovered using the student psychology-based optimisation (SPBO) method, which was used for feature selection. A BGRU model was used for disease classification, which successfully captured sequential dependencies in patient data and reduced the problems associated with gradients that are common in traditional recurrent neural networks. To further enhance model efficiency and diagnostic accuracy, the SA-GSO method was used to optimise hyperparameter tuning. Diabetes prediction can be automated and improved with the help of the suggested method, which provides a solid foundation for medical applications. In order to make the dataset more applicable in clinical practice, further study will involve investigating different imputation approaches, reviewing more feature selection methodologies, and increasing the dataset's diversity.