Year: 2025
Type: article
Abstract: This study presents a novel approach to predict gestational diabetes using Graph Neural Networks (GNNs) and graph theory. The dataset, sourced from a medical database, includes features like age, pregnancies, BMI, HDL levels, and blood pressure. Data preprocessing involves filling missing values with column means and standardizing features. A graph is constructed with patients as nodes, each characterized by their features and class labels (GDM or Non-GDM). Edges represent significant relationships based on Euclidean distance. Visualization using a spring layout reveals clusters of similar patients, enhancing understanding of gestational diabetes predictors and potentially improving early diagnosis and patient management
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4410097436