Leveraging a hybrid convolutional gated recursive diabetes prediction and severity grading model through a mobile app.

ABSTRACT: Diabetes mellitus is a common illness associated with high morbidity and mortality rates. Early detection of diabetes is essential to prevent long-term health complications. The existing machine learning model struggles with accuracy and reliability issues, as well as data imbalance, hindering the creation of a dependable diabetes prediction model. The research addresses the issue using a novel deep learning mechanism called convolutional gated recurrent unit (CGRU), which could accurately detect diabetic disorder and their severity level. To overcome these obstacles, this study presents a brand-new deep learning technique, the CGRU, which enhances prediction accuracy by extracting temporal and spatial characteristics from the data. The proposed mechanism extracts both the spatial and temp

SUBMITTER: Alsayed AO 

PROVIDER: S-EPMC11888868 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature