Prognosis Model for Gestational Diabetes Using Machine Learning Techniques

Gestational diabetes mellitus (GDM) is a syndrome that occurs among women during pregnancy and is characterized by lack of insulin hormone secretion. GDM occurs in about 4% of all pregnancies and is diagnosed at later stages of pregnancy. It can occur in women with no known history of diabetes. Since no signs or symptoms occur at the onset of GDM, it is possible to diagnose it only through screening tests. GDM poses some major health risks such as hormonal imbalance, delivery risks, and the development of Type 2 diabetes ( T2D) after delivery. The condition can be diagnosed from the blood sugar level. Those diagnosed with GDM are likely to be obese, have a weak constitution, and be undergoing a stressful life or living in a stressful environment, eating unhealthy food, and living an unhealthy lifestyle. Other risk factors to be considered are family history, heredity, and the occurrence of diabetes in the past. Apart from diagnosis, the most crucial stage in managing GDM is its prognosis. If the disease is diagnosed at earlier stages, one can avoid its complications. Advanced technologies such as IoT and wearable sensors can help healthcare professionals in identifying the early signs and symptoms of GDM. In this scenario, data mining techniques are recommended for the prognosis of GDM using existing medical reports and risk factors related to women. A patient's medical history and their family history should be correlated with each other to find the likelihood of GDM occurrence. Classification is a technique in which a training dataset is used to predict the importance of related factors using an inference function. Our aim is to develop a prognosis model for GDM using a classification technique. A GDM prognosis model is developed using a training set of disease parameters along with an individual's risk factors. From the results of our experiments, it is inferred that the proposed model can be used for predicting the likelihood of GDM in its earlier stages.