Optimizing Fetal Health Diagnosis: An Active Learning Framework with LightGBM
Sep 01, 2025
DOI: 10.1109/IDSTA66210.2025.11202834
Published in: 2025 Sixth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)
Publisher: IEEE
Fetal health classification is crucial for the timely identification of abnormalities and the improvement of neonatal care. Early prediction of fetal health is necessary to ensure a healthy pregnancy and lower rates of maternal and newborn mortality. Machine learning algorithms improve fetal health monitoring by enabling early detection of abnormalities and facilitating timely medical interventions. However, traditional machine learning models rely on large labeled datasets, which are often costly and time-consuming to obtain in medical applications. Active learning (AL) mitigates this challenge by strategically selecting the most informative samples for annotation, significantly reducing the labeling effort. In this paper, we present an active learning approach coupled with LightGBM (Gradient Boosting Machine) that achieves more than 99% classification accuracy with merely 16% of training data thus significantly reducing data annotation cost and effort.