Ensemble Learning for Enhanced Classification of Fetal Health Using Cardiotocography Data

Abstract: Accurate classification of fetal well-being is required to detect early possible risks in pregnancy. Fetal monitoring is a crucial task performed by cardiotocography (CTG), which records fetal heart rate and uterine contractions. CTG reading manually is subjective and liable to mistake. In this work, the authors propose an ensemble learning approach, combining Random Forest and XGBoost models, to enhance fetal well-being classification as normal, suspect, and pathological. Through aggregation of the capabilities of different classifiers, the ensemble model yields enhanced accuracy and reliability over traditional single algorithms. Comprehensive testing with cross-validation confirms the stability of the proposed method. The findings suggest the potential of ensemble learning in supporting healthcare assessments, thereby improving prenatal care and enabling early clinical decision-making.