Early Detection of Cardiac Arrest in Newborns Using Machine Learning

Abstract

Cardiac arrest in neonates is a frequent and critical health issue that requires immediate and
effective intervention to reduce mortality and morbidity rates. Early detection is crucial for
providing optimal care and treatment for these infants. Current research has focused on
identifying potential biomarkers and signs of cardiac arrest in newborns, alongside developing
precise and efficient diagnostic techniques. However, the integration of advanced machine
learning algorithms into the detection process is relatively unexplored. This study aims to
develop a Cardiac Machine Learning Model (CMLM) that can accurately detect cardiac arrest
in newborns within the Cardiac Intensive Care Unit (CICU) by analyzing physiological
parameters using statistical models. The CMLM leverages logistic regression and support
vector machines to build predictive models based on a comprehensive dataset of neonates'
physiological data. This approach promises to enhance the prompt detection of cardiac arrest,
thereby expediting treatment and improving outcomes. The proposed methodology, when
implemented in the CICU, is expected to significantly reduce the morbidity and fatality rates
of neonates due to cardiac arrest. By integrating machine learning with traditional diagnostic
techniques, this research provides a robust framework for early cardiac arrest detection in
newborns, paving the way for more timely and effective medical interventions.