TY  - JOUR
AU  - G Raju
AU  - Sugur Sandeep Kumar
AU  - S Tharun Kumar Reddy
AU  - Thota Harsha Vardhan Reddy
AU  - Kancherla Srujan Paul
PY  - 2026
DA  - 2026/04/25
TI  - Machine Learning Based Prediction Of Cardiac Arrest In Newborn Babies
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 6
IS  - 4
AB  - Cardiac arrest in newborn babies is a life-threatening medical emergency that requires early detection 
and prompt treatment to reduce mortality and long-term health complications. Traditional monitoring 
methods used in neonatal intensive care units mainly rely on manual observation and basic clinical 
indicators, which often fail to identify subtle and early changes in a baby’s vital signs, leading to 
delayed diagnosis and reduced survival chances. To overcome these limitations, the developed model 
proposes a machine learning–based system using statistical models for the early detection of cardiac 
arrest in newborn babies. The proposed system analyzes key physiological parameters such as heart 
rate, respiratory rate, oxygen saturation, and medical history data to identify early warning signs 
and potential risk factors. Machine learning techniques including Logistic Regression and Support 
Vector Machines are applied to detect hidden patterns that are difficult to recognize using conventional 
methods. The system enables automated, accurate, and continuous monitoring in the Cardiac Intensive 
Care Unit (CICU), supporting early clinical decision-making and timely medical intervention. By 
reducing reliance on manual monitoring, improving prediction accuracy, and minimizing response time, 
the proposed approach enhances the quality of neonatal care and increases the chances of survival for 
newborn babies through intelligent, data-driven healthcare solutions.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1301
DO  - 10.33425/3066-1226.1301