Authors - Bhuvaneswari Perumal, Vaishnavi Moorthy, Gladius Jennifer H Abstract - According to UNICEF-India, 46% of maternal fatalities and 40% of neonatal fatalities transpire during labor or within the initial 24 hours post-delivery. Antepartum care includes routine surveillance, assessment of risks, and appropriate actions to enhance the health of the mother and fetus. Intrapartum care provides for safe labour and delivery with surveillance and appropriate management of complications by skilled personnel.This study aims at identifying the importance of holistic care in these stages and how it helps in preventing complications through the identification of high risk pregnancies which will be useful in avoiding the development of severe problems in future. The study uses analytical tools and Machine Learning models to analyze the health data and risk factors of pregnancy. In existing state of art they have inadequate early risk prediction with poor personalization. So the collected data includes several risk factors identified and classified based on their level of risk. The results of the attempts of applying various machine learning models and EDA methods to define the most important risk factors. This is a very large reduction and in line with the United Nations Sustainable Development Goals for the year 2030.The aim is to reduce maternal and neonatal morbidity and mortality. Lack of adequate management of intrapartum care can lead to postpartum problems to a large extent and thus affect the prenatal and fetal well-being.
