Predicting Maternal Health Risks and Fetal Impacts using Machine Learning

The research study applies machine learning algorithms to produce simultaneous forecasts about maternal and fetal

health in an innovative integrated approach. We implemented and deployed two primary models including one for fetal health

classification and another for pregnant health prediction. The research objective involves identifying maternal health risks

followed by assessing their influence on fetal health results. Our system operates through a user-friendly web-based platform

based on Streamlit development that enables medical practitioners to easily interact with and review analytical results. The fetal

health categorization model analyzes full cardiotocography (CTG) records yet the pregnancy risk prediction model works with

crucial maternal health measurements. The integrated approach demonstrates its ability to deliver rapid and exact risk

assessments through the findings that could boost maternal care choices while improving health results for mothers alongside

their newborns.