One of the disastrous but common medical emergencies is cardiac arrest in newborn babies. It is essential to ensure that such babies have the best treatment and care by identifying them early enough. The recent research has been aimed at pinpointing the possible indicators and biomarkers of cardiac arrest in newborn infants and creating effective and precise diagnostic procedures to make timely diagnosis. A wide range of visualization methods, including echocardiography and computed tomography can assist in the early warning of cardiac arrest. The purpose of We is to build Detection model and Sevierity Prediction model with ANN to predict cardiac arrest in newborn babies early in the Cardiac Intensive Care Unit (CICU). The incidences of cardiac arrest were determined by using a combination of the physiological parameters of the neonate. The CICU will apply the suggested model to empower early identification of cardiac arrest among the newborn babies.

Detecting cardiac arrest in newborn babies is a critical challenge in neonatal care, demanding swift and accurate intervention to prevent severe outcomes. Artificial Neural Networks (ANNs) offer a promising avenue for improving early detection in this vulnerable population. By leveraging large datasets comprising physiological parameters, ANNs can learn intricate patterns indicative of cardiac distress, enabling timely intervention. ANN’s excel at recognizing complex, nonlinear relationships within data, making them adept at discerning subtle indicators of cardiac arrest in newborns. Parameters such as heart rate variability, respiratory patterns, and oxygen saturation levels can be analyzed comprehensively by ANNs, allowing for the identification of pre-arrest patterns and facilitating proactive measures. Moreover, ANNs have the potential for continuous monitoring, providing real-time assessment and alerts to healthcare providers, thus enhancing vigilance and response times. Their adaptability to diverse clinical settings and the ability to integrate with existing monitoring systems make ANNs a viable solution for enhancing neonatal care. In conclusion, the application of ANNs holds significant promise in the early detection of cardiac arrest in newborn babies, offering the potential to save lives and improve long-term outcomes through timely intervention and effective management of critical events.

1.M. Hegazy, S. Ghaleb, and B. B. Das, ‘‘Diagnosis and management ofcancer treatment-related cardiac dysfunction and heart failure in children,’’Children, vol. 10, no. 1, p. 149, Jan. 2023. 2.C. J. Winslow, D. P. Edelson, M. M. Churpek, M. Taneja, N. S. Shah, A. Datta, C.-H. Wang, U. Ravichandran, P. McNulty, M. Kharasch, and L. K. Halasyamani, ‘‘The impact of a machine learning earlywarning score on hospital mortality: A multicenter clinical intervention trial,’’ Crit. CareMed., vol. 50, no. 9, pp. 1339–1347, Sep. 2022. 3.C. Luo, Y. Zhu, Z. Zhu, R. Li, G. Chen, and Z.Wang, ‘‘A machine learningbasedrisk stratification tool for in-hospital mortality of intensive careunit patients with heart failure,’’ J. Transl. Med., vol. 20, no. 1, pp. 1–10,Dec. 2022. 4.M. S. Bleiweis, J. C. Fudge, G. J. Peek, H. V. Vyas, S. C. Beltran,A. D. Pitkin, K. J. Sullivan, J. F. Hernandez-Rivera, J. Philip, andJ. P. Jacobs, ‘‘Ventricular assist device support in neonates and infants witha failing functionally univentricular circulation,’’ JTCVS Techn., vol. 13,pp. 194–204, Jun. 2022. 5.E. Vasichkina, D. Alekseeva, V. Karev, E. Podyacheva, I. Kudryavtsev,A. Glushkova, A. Y. Starshinova, D. Kudlay, and A. Starshinova, ‘‘Cardiacinvolvement in children affected by COVID-19: Clinical features anddiagnosis,’’ Diagnostics, vol. 13, no. 1, p. 120, Dec. 2022. 6.S. R. Rooney, E. L. Reynolds, M. Banerjee, S. K. Pasquali, J. R. Charpie, M. G. Gaies, and G. E. Owens, ‘‘Prediction of extubation failure in the paediatric cardiac ICU using machine learning and high-frequency physiologic data,’’ Cardiol. Young, vol. 32, no. 10, pp. 1649–1656, Oct. 2022. 7.M. Moor, B. Rieck, M. Horn, C. R. Jutzeler, and K. Borgwardt, ‘‘Earlyprediction of sepsis in the ICU using machine learning: A systematicreview,’’ Frontiers Med., vol. 8, May 2021, Art. no. 607952.