Exploring Machine Learning Classifiers for Identifying Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a con-dition in which a hormone produced by the placenta disrupts the body's ability to use insulin efficiently. Early diagnosis of GDM risk is essential since it can lead to more effective treatments and less cumulative effects. The purpose of this study aims to investigate an efficient model for early identification of GDM using commonly accessible parameters to enable early intervention. The project aims to use prediction algorithms in locations where more thorough assessments are unavailable. Multiple classifiers are created that function well in diagnosing GDM having accuracies ranging from 93% to 90.5%. KNN and XGB classifiers achieved 95% accuracy, which is the best among all classifiers. This suggests employing these classifiers to create a GDM detection system.