A Path to Improved Fetal Cardiovascular Health Outcomes Using Machine Learning

Reduction of child mortality is a significant focus of the UN's Sustainable Development Goals (SDGs). The UN seeks to eliminate preventable neonatal and under-5 deaths by 2030 [1]. Maternal mortality, which accounted for 295,000 deaths during and following pregnancy and childbirth in 2017, is closely linked to child mortality. Most of these deaths (94 percent) occurred in low-resource settings and were preventable [2]. Cardiotocograms (CTG) offer a simple and cost-effective method to assess fetal health, which is crucial in preventing child and maternal mortality. CTG equipment works by sending ultrasound pulses and analyzing the responses to monitor fetal heart rate (FHR), fetal movements, uterine contractions, and other parameters in the dataset. Using CTG data, machine learning models—Multinomial Logistic Regression, Random Forest, Gradient Boosting and Fully Connected Feedforward Neural Network (FCNN)—were employed to classify fetal health into three categories: Normal, Suspect, and Pathological. Strategic feature preprocessing and class-balancing techniques were implemented to enhance model performance. Results indicate that FCNN achieved the highest overall accuracy, while Gradient Boosting provided superior recall for pathological cases. Additionally, FCNN demonstrated strong recall for the suspect category, ensuring accurate identification of at-risk cases. These findings suggest that FCNN is an effective model for fetal health classification, with Gradient Boosting serving as a strong alternative for prioritizing recall of clinical scenarios. By minimizing observer dependency, the study aims to mitigate unnecessary medical interventions while providing a consistent, accurate, and cost-effective approach to neonatal health assessment [3].