Predicting Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child Using Machine Learning

Abstract

Abstract: One of the main causes of health issues for expectant mothers and their unborn children is diabetes during pregnancy. Machine learning is a crucial technique for estimating the probability of such a development based on the provided data, as gestational diabetes might advance to permanent diabetes. Pregnancyrelated diabetes may be predicted by the current study, but neonatal diabetes risk cannot be predicted. Therefore, in order to deliver the most precise results about diabetes persistence in pregnant women and to enhance the forecasting of neonatal mellitus, new characteristics are needed. This may be accomplished with the use of Python scripting and machine learning techniques like K Nearest Neighbors, Support Vector Machines, and Logistic Regression. The preprocessed machine learning dataset on diabetes was gathered via Kaggle and came from the Pima Indian diabetes database. Additionally, the project's dataset now includes two additional attributes. Research suggests that machine learning models using features like SVM and decision trees may be able to accurately predict a pregnant woman's probability of developing diabetes. Numerous variables have been employed to forecast when this illness may manifest during pregnancy..