A Network Approach To Predict GDM Risk In Pregnant Women

Abstract

High blood sugar during pregnancy known as gestationaldiabetes mellitus (GDM) can cause difficulties forboth the mother and the unborn child. Particularly inplaces where prenatal care is scarce, early detection andmanagement are essential. This study suggests a combinedmachine learning prediction model to determine whichexpectant mothers are susceptible to gestational diabetesmellitus. We examined eight distinct models, incorporatingdeep learning methodologies.(Artificial Neural Networks)and conventional machine learning algorithms (SupportVector Machine, Naive Bayes, Random Forest, and LogisticRegression), using a dataset of 3526 pregnant womenfrom Kaggle’s Gestational Diabetes Mellitus dataset. Withaccuracy rates ranging from 87% to 97%, these modelsdemonstrate the immense potential of machine learning toenhance GDM screening and management, especially inresource-constrained environments