A Comprehensive Approach to Fetal Health Prediction Using Machine Learning and Ensemble Models

Fetal health is a critical factor during pregnancy, and early identification of potential risks is essential for ensuring the well-being of both the mother and the unborn child. Traditional methods like cardiotocography (CTG) and ultrasound, though widely used, are often subject to human error, especially in resource-constrained settings. The advancement of artificial intelligence (AI) and machine learning (ML) presents new opportunities for automating fetal health risk prediction. In this study, we explore several ML algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and ensemble methods like AdaBoost and Gradient Boosting. Our goal is to reduce human error, improve diagnostic accuracy, and enable remote health monitoring. Through comparative analysis, we find that ensemble methods, particularly AdaBoost, achieve the highest accuracy and F1 scores in predicting fetal health risks. These findings demonstrate the potential of AI-based systems in enhancing fetal monitoring, particularly in areas with limited healthcare access, leading to better clinical decision-making and outcomes.