Cardiotocography Data Analysis for Fetal Health Classification Using Machine Learning Models

Abstract

Pregnancy complications pose significant risks to maternal and fetal health, necessitating early detection for timely interventions. Manual analysis of cardiotocography (CTG) tests, the conventional practice among obstetricians, is labor-intensive and prone to variability. This study addresses the critical need for accurate fetal health classification using advanced machine learning (ML) techniques, focusing on the application of XGBoost, a powerful gradient boosting algorithm. Utilizing a publicly available dataset, despite its size, this research leverages its rich features to develop and analyze ML models. The objective is to explore and demonstrate the efficacy of ML models in classifying fetal health based on data. Our proposed system applies the XGBoost algorithm and achieves an exceptional accuracy of 96%, surpassing previous methods. This highlights the algorithm's robustness in enhancing diagnostic precision and facilitating timely interventions. The study underscores the potential of integrating ML models into routine clinical practices to streamline fetal health assessments. By optimizing resource allocation and improving time efficiency, these models contribute to early complication detection and enhanced prenatal care. Further research is encouraged to refine ML applications, promising continued advancements in fetal health assessment and maternal care.