Utilizing Multimodal Medical Data and a Hybrid Optimization Model to Improve Diabetes Prediction

Abstract

Diabetes is a major health issue that affects people all over the world. Accurate early diagnosis is essential to enabling adequate therapy and prevention actions. Through the use of electronic health records and recent advancements in data analytics, there is growing interest in merging multimodal medical data to increase the precision of diabetes prediction. In order to improve the accuracy of diabetes prediction, this study presents a novel hybrid optimisation strategy that seamlessly combines machine learning techniques. In order to merge many models in a way that maximises efficiency while enhancing prediction accuracy, the study employs a collaborative learning technique. This study makes use of two separate diabetes database datasets from Pima Indians. A feature selection process is used to streamline error-free classification. A third method known as Binary Grey Wolf-based Crow Search Optimisation (BGW-CSO), which was produced by merging the Binary Grey Wolf Optimisation Algorithm (BGWO) and Crow Search Optimisation (CSO), is provided to further enhance feature selection capabilities. This hybrid optimisation approach successfully solves the high-dimensional feature space challenges and enhances the generalisation capabilities of the system. The Support Vector Machine (SVM) method is used to analyse the selected characteristics. The performance of conventional SVMs is enhanced by the newly created BGW-CSO technique, which optimises the number of hidden neurons within the SVM. The proposed method is implemented using Python software. The suggested BGW-CSO-SVM approach outperforms the current methods, such as Soft Voting Classifier, Random Forest, DMP_MI, and Bootstrap Aggregation, with a remarkable accuracy of 96.62%. Comparing the suggested BGW-CSO-SVM approach to the other methods, accuracy shows an average improvement of around 16%. Comparative evaluations demonstrate the suggested approach's improved performance and demonstrate its potential for real-world use in healthcare settings.