Machine Learning Applications in Predictive Fetal Health Assessment and Early Intervention

Fetal health involves monitoring the well-being of the unborn baby, identifying potential complications such as distress, congenital issues and ensuring timely interventions for a healthy pregnancy and successful delivery. This study presents a machine learning framework aimed at improving fetal health predictions with a focus on the need for early detection of complications like fetal distress. Traditional monitoring tools such as cardiotocography and ultrasound, often rely on subjective interpretation, leading to delays or inaccuracies in diagnoses. To tackle these issues, advanced machine learning techniques such as XGBoost, Blending, and Stacking are applied. These models are assessed for their ability to handle complex fetal health data and improve classification accuracy. While Random Forest and K-Nearest Neighbors show moderate success, XGBoost, Blending, and Stacking perform better, achieving 96% accuracy, reducing errors and enhancing clinical decision-making. For small datasets and generalization issues and using multiple fetal health indicators for stronger predictions. Automated machine learning models offer significant improvements in the speed and precision of fetal health assessments, helping reduce risks for both mother, child and advancing AI applications in prenatal care for improved outcomes.