Predicting Fetal Health using Machine Learning on Mitigate Child and Maternal Mortality

Abstract

This study investigates the application of machine learning techniques to predict fetal health using Cardiotocogram (CTG) data, a critical tool in obstetrics for monitoring fetal well-being. CTG captures vital indicators such as fetal heart rate (FHR), uterine contractions, and fetal movements, which are essential for assessing the fetus’s condition during pregnancy and labor. Early detection of potential fetal distress is crucial in preventing complications and reducing maternal and neonatal mortality rates. However, the manual interpretation of CTG data can be time-consuming and error-prone, highlighting the need for automated solutions. In this study, a variety of machine learning models were applied to a CTG dataset after preprocessing to address class imbalances and optimize feature selection. The models were evaluated based on accuracy and ROC AUC (Receiver Operating Characteristic - Area Under Curve) scores. The XGBoost and LightGBM models demonstrated exceptional performance, achieving accuracies of 98.11% and 97.84%, respectively, along with near-perfect ROC AUC scores of 0.9985 and 0.9984, indicating their ability to reliably distinguish between the three fetal health categories: Normal, Suspect, and Pathological. These results highlight the potential of XGBoost and LightGBM as highly effective tools for real-time fetal health assessment, offering significant advantages over traditional manual methods. This approach not only provides accurate predictions but also presents a scalable and efficient solution for

resource-constrained healthcare settings. By enhancing medical decision- making through automated fetal health monitoring, this study aims to contribute to the reduction of preventable maternal and neonatal deaths, particularly in low-resource environments where timely interventions are critical.