Enhanced Fetal Health Prediction using Deep Learning and Ensemble Methods

This study aims to enhance fetal health prediction by employing deep learning models and ensemble methods to analyze cardiotocography (CTG) data. Traditional methods often overlook subtle indicators of fetal distress, leading to inadequate monitoring. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, our approach seeks to improve diagnostic accuracy. We will thoroughly discuss the dataset utilized, detailing preprocessing techniques to ensure optimal data quality. Performance benchmarks will be established against traditional machine learning models, paving the way for a comprehensive evaluation of accuracy and precision in fetal health predictions. This research not only highlights the significance of integrating advanced methodologies but also presents a novel approach to fetal monitoring, thereby contributing to better maternal and neonatal health outcomes.