A Proficient Multi-level Data Analytic Suite for Ascertaining Preliminary Gestational Hazards Associated with Its Influences

The basis of sustaining positive health outcomes begins from womb in the current era is safeguarding pregnancy from problems including maternal mortality and morbidity. A pregnancy with a hypertensive issue and diabetes is regarded as having a very high risk since it may result in long-term diseases such cardiovascular disease, obesity due to fetal growth restriction, anemia, and thyroid problems. Low birth weight, early delivery, and other serious neonatal disorders can affect babies who go untreated throughout pregnancy. Advances in Intelligence system and health data analytics allow for the early diagnosis of pregnancy concerns, reducing risks. This proposed effort includes the prediction of early gestational problems like diabetes and hypertension as well as the analysis of the risk variables that may be present. In our suggested work, multi-level data analysis is employed to better understand the health of pregnant women throughout the first trimester. In this data analysis suite, summarizing of data with deep insights is done at data preparation using exploratory data analysis (EDA) to look at the core pattern of data with its properties. The KNN algorithm has been used to preprocess data. Pregnancy risks including gestational hypertension and diabetes are identified before 13 weeks of pregnancy machine learning techniques. In order to comprehend analytical data with graphical representations, visual analytical approaches have been utilized. As a consequence, 97.18% accuracy is achieved.