Fetal health prediction using machine learning

Abstract

Fetal health assessment plays a crucial role in prenatal care, as early detection of fetal distress can help prevent complications and ensure better outcomes for both the mother and baby. Traditionally, this assessment relies on cardiotocography (CTG) data, which medical professionals analyze to determine fetal well-being. However, this process can be subjective and influenced by individual expertise. Machine learning presents a valuable opportunity to enhance this assessment by automating classification, reducing variability, and improving diagnostic accuracy. By recognizing subtle patterns in CTG data that may be difficult for the human eye to detect, these models can provide more consistent and reliable evaluations. This paper explores the application of various machine learning models for fetal health classification, comparing their effectiveness in identifying normal, suspected, and pathological cases. Additionally, we discuss key aspects such as feature selection, model interpretability, and the potential for integrating these approaches into clinical practice to support healthcare professionals in making informed decisions.