AI-Driven Insights into Fetal Genomics for Predicting Developmental Anomalies

This paper improves fetal health monitoring: The goal is to transform prenatal healthcare by developing a cutting-edge software platform for ongoing fetal health monitoring and analysis using a Django Server for Comprehensive Tracking and Analysis. This system handles and analyzes IoT sensor data on fetal health measures, including as heart rate, movement, and other critical indicators, using a robust, scalable backend server built with the Django web framework. Real-time data collection, storing, and analysis are done by Django's core server. The system uses machine learning and contemporary data processing techniques to identify health-related patterns. By giving physicians useful information, these tools help them identify fetal abnormalities early and improve outcomes. Expectant parents and medical professionals can view real-time health metrics on the website's user-friendly dashboards and graphs. The project's major component is its intuitive desktop and mobile web interface. Parents can track their unborn kid through this interface, and clinicians can view patient data in real time, analyze trends, and make informed decisions. Proper actions are taken on time through messages or alerts for abnormal readings with the help of the intelligent alert mechanism of the system. This real-world application of health care based on Django bridges with IoT data collection for an all-inclusive and effective fetal health monitoring program. Real-time monitoring, intelligent analytics, and easily available visualizations enhance prenatal care through providing early and accurate insights toward improving maternal and fetal outcomes.