SDAGS: SMOTE + forest diffusion-based data augmentation and GBT-based stacking ensemble learning for holistic AI-powered diabetes mellitus prediction - Nguyen, An K.; Gong, Bi Y.; Trinh, Binh T.; Le Hong, Thu; Nguyen Tue, Minh; Du Phuong, Hanh (Journal of Computer Science and Cybernetics, 2024)
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%, respectively<a id="component-grid-users-author-authorgrid-orderItems-button-65f6753e4f83f" class="pkp_controllers_linkAction pkp_linkaction_orderItems pkp_linkaction_icon_order_items" href="https://vjs.ac.vn/index.php/jcc/submission/wizard/2?submissionId=20361#">Order