LEVERAGING MACHINE LEARNING TO IDENTIFY MATERNAL RISK FACTORS FOR CONGENITAL HEART DISEASE IN OFFSPRING

Congenital Heart Defects (CHDs) represent a significant global health challenge, being the most common birth anomalies. Early identification of mothers at risk of having a child with a CHD is crucial for timely intervention, improved prenatal counseling, and better neonatal outcomes. This article explores the application of machine learning (ML) methodologies to predict the risk of CHDs in offspring based on maternal characteristics and health data. We review various ML algorithms, including traditional classifiers and advanced neural networks, that have been or could be employed for this predictive task. Key aspects of data collection, preprocessing, feature engineering, and model evaluation are discussed within the context of identifying relevant maternal risk factors. By analyzing existing literature and outlining potential experimental frameworks, this study highlights the immense potential of ML in augmenting clinical decision-making, facilitating early risk stratification, and ultimately contributing to improved maternal and child health outcomes concerning CHDs.