Analysis of Gestational Diabetes using Data Mining Algorithms

Abstract: Data Mining is used to discover new patterns from large volume of data. In data mining the classification is very important task. Gestational diabetes is a condition characterized by high blood sugar levels that is first predictable during pregnancy period of a woman. Diabetes is a disease in which levels of blood glucose, also called blood sugar, are above normal. People with diabetes have problems converting food to energy. Generally, after a meal, the body breaks food down into glucose, which the blood carries to cells throughout the body. Cells use insulin, a hormone made in the pancreas, to help them convert blood glucose into energy. Through the second and third trimester, a mother's diabetes can lead to over-nutrition and excess growth of the baby. Having a large baby rises risks during labour and delivery. In addition, when foetal over-nutrition occurs and hyper insulinemia results, the baby's blood sugar can drop very low after birth, since it won't be receiving the high blood sugar from the mother. However, with proper treatment, a gestational diabetic mother can deliver a healthy baby despite having diabetes. In this paper, many classification algorithms like J48, simple CART and Naïve bayes algorithm are used to diagnose the diabetes in pregnant women and they are compared for their accuracy levels.

Keywords: Data Mining, Classification, Gestational Diabetes, Blood glucose, J48, CART, Naïve bayes.