Machine Learning Revolutionizing in Gestational Diabetes Care

Significant progress in the field of biotechnology and the development of robust public healthcare systems have resulted in the generation of vast amounts of valuable healthcare data. Through the application of diverse data analysis methods, researchers have discovered intriguing patterns that aid in the timely detection and prevention of severe diseases. Diabetes mellitus is one such condition; it arises when the body either doesn’t create enough insulin (a hormone that controls blood sugar) or doesn’t properly use the insulin it does make. This causes a blood sugar imbalance, which in turn increases the risk of cardiovascular disease, renal damage, and neurological impairment. Type 1 diabetes mellitus is the most common form of the disease. All three forms of diabetes: type 1, type 2, and pregnancy related. Various machine learning techniques have been suggested to classify, identify at an early stage, and predict diabetes, offering promising approaches to address this critical health concern. To classify diabetes, a set of six different classifiers are employed: Decision Trees, KNN, Naive Bayes, Support Vector Machines, Random Forests, and Logistic Regressions. To enhance the accuracy of diabetes classification, a prediction model incorporating external factors associated with diabetes, in addition to regular factors such as Glucose, BMI, Age, and Insulin, will be utilized. The dataset will undergo training and testing to obtain reliable and precise results of effective prediction.