Comparative Study of Machine Learning Models for Early Gestational Diabetes Mellitus

Gestational Diabetes Mellitus is a common condition that affects a significant proportion of pregnant women. Rather than being taken in by the cells, glucose accumulates in the bloods flow, leading to an elevated concentration in the blood. Due to the collapse of the normal absorption process of glucose, this condition occurs. GDM is glucose intolerance occured during pregnancy. Computer-Aided Diagnosis is a research Field in the medical business that is expanding very fast. At present Machine learning is a new focus area to early diagnose diseases. In this paper pregnancy complications are predicted through the utilization of a decision tree, Random Forest, and XGBoost to enable early treatment initiation and mitigate their impact on both the mother and the child. Recent advances in machine learning research promise to increase the accuracy of disease perception and diagnosis. The target is, to sum up the knowledge of GDM. Models of machine learning that substantially impact the identification and diagnosis of diabetes are the main topic of this paper.