International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The world's future relies on ensuring children are born without complications, yet many suffer from disorders like brain injury or stillbirth caused by fetal distress due to insufficient oxygen supply during delivery. Traditional Cardiotocography (CTG) is a widely used method to analyze the fetal heart rate (FHR) and the mother's uterine contractions. However, its interpretation is often subjective and dependent on clinician expertise, leading to inter- and intra-observer variation and high false-positive rates. This project addresses this critical gap by developing a more reliable, accurate, and automated system for monitoring fetal well-being using CTG data. The solution leverages machine learning (specifically, the Random Forest Classifier) and signal processing techniques to analyze key physiological parameters, aiming to reduce human error, standardize diagnosis, and provide real-time alerts for timely clinical interventions. Ultimately, this initiative seeks to enhance the quality and accessibility of prenatal care, contributing to better maternal and neonatal outcomes.

Keywords: Fetal Distress, Cardiotocography (CTG), Fetal Heart Rate (FHR), Machine Learning, Random Forest, Automation

Downloads: | DOI: 10.17148/ IJARCCE.2025.141017