SDAGS: SMOTE+FOREST DIFFUSION-BASED DATA AUGMENTATION AND GBT-BASED STACKING ENSEMBLE LEARNING FOR HOLISTIC AI-POWERED DIABETES MELLITUS PREDICTION

Abstract

It's critical to track and anticipate diabetes in emerging nations like Vietnam, particularly for those with type 1 diabetes. This article proposes SDAPS, an AI-powered diabetes prediction technique. Our method is based on two ideas: (i) using the SFDM method to make the training data better by combining the oversampling of the Forest Diffusion Model with the SMOTE data balancing method; and (ii) making the GSDP model by stacking different boosting machine learning models together. We also suggest an AI-powered blood glucose monitoring and recommendation system based on SDAPS to provide diabetic patients with all-encompassing assistance with blood glucose monitoring, dietary counseling, physical activity, and the proper use of medications. Our thorough experiments using the Pima Indians diabetes dataset and the 5-fold cross-validation evaluation method demonstrate that SDAPS outperforms the state-of-the-art methods, with the following results: sensitivity, specificity, F1, accuracy, and precision: 98.03\%, 99.49%, 98.74%, 98.75%, and 98.00%, respectivelyOrder