Deep Neural Networks for Fetal Health Monitoring Through Cardiography Data Analysis

In prenatal treatment necessitating the development of sophisticated approaches like deep neural network-based cardiography analysis to improve outcomes. This project examines prenatal health monitoring through cardiography data analysis applying deep neural networks. We propose a unique deep neural network approach trained on the fetal cardiotocography dataset obtaining an amazing 97% accuracy in forecasting prenatal health risk levels. Leveraging different cardiography characteristics. Our methodology provides comprehensive risk identification and preemptive healthcare treatments. Our strategy shows potential for transforming prenatal care practices and increasing maternal-fetal outcomes. This study serves as a cornerstone for future breakthroughs in prenatal care delivery. It offers enormous potential for enhancing worldwide maternal and fetal health standards.