Fetal Health Classification Based on CTG

Abstract

This research paper explores the application of advanced machine learning techniques for fetal health detection using cardiotocography (CTG). Cardiotocography is a pivotal tool in monitoring fetal and maternal health during pregnancy, providing crucial insights into fetal heart rate patterns and uterine contractions. In this work, various predictive models, including logistic regression, nearest neighbors, and gradient boosting classifiers, to analyze CTG data were implemented. The findings indicate that these models can effectively classify fetal health status, with gradient boosting demonstrating the highest predictive accuracy. This work highlights the potential of integrating machine learning methodologies into clinical practice to enhance fetal monitoring, ultimately improving maternal and fetal health outcomes.