Ritu,
Anshika,
- Assistant Professor, Department of Nursing, Galgotias University, Uttar Pradesh, India
- Nursing Tutor, Department of Nursing, Galgotias University, Uttar Pradesh, India
Abstract
The care of newborn infants remains a significant responsibility for healthcare professionals, as ensuring infant survival can often be complex and demanding. Conditions such as heart failure and cardiac arrest in infants are life-threatening and require prompt diagnosis and treatment. Detecting cardiac problems at an early stage can greatly enhance survival outcomes and minimize serious complications. In recent years, machine learning (ML) approaches have become valuable tools for predicting cardiovascular diseases by examining medical records and physiological data. This study emphasizes the early detection of heart failure in infants through the application of ML techniques. Various cardiac indicators and infant health parameters were evaluated to determine the probability of heart failure and cardiac arrest. The findings revealed that the identified rate of cardiac arrest risk was 58.78%, whereas 46.67% of the cases showed no evidence of cardiac abnormalities. Various performance parameters, such as false rate, false observation rate, stability rate, and precision rate, were also evaluated. The stability rate achieved was 1.67%, and the precision rate was 60.039%. The findings of this study may help healthcare professionals in identifying and managing different levels of heart failure in newborn babies at an early stage, thereby improving infant healthcare outcomes.
Keywords: Machine learning, heart failure, infants, cardiac arrest, prediction, healthcare, precision rate
[This article belongs to International Journal of Emergency and Trauma Nursing and Practices ]
References
- Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358. doi:10.1056/NEJMra1814259. PubMed: 30943338.
- Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–2679. doi:10.1016/j.jacc.2018.03.521. PubMed: 29880128.
- Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi:10.1038/s41591-018-0300-7. Epub. PubMed: 30617339.
- Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29. doi:10.1038/s41591-018-0316-z. Epub. PubMed: 30617335.
- Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70–74. doi:10.1038/s41591-018-0240-2. Epub. PubMed: 30617318.
- Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731. doi:10.1038/s41551-018-0305-z. Epub. PubMed: 31015651.
- Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–1930. doi:10.1161/CIRCULATIONAHA.115.001593. PubMed: 26572668. PMCID: PMC5831252.
- Saqib M, Perswani P, Muneem A, Mumtaz H, Neha F, Ali S, Tabassum S. Machine learning in heart failure diagnosis, prediction, and prognosis: Review. Ann Med Surg (Lond). 2024;86(6):3615–3623. doi:10.1097/MS9.0000000000002138. PubMed: 38846887. PMCID: PMC11152866.
- Shillan D, Sterne JAC, Champneys A, Gibbison B. Use of machine learning to analyse routinely collected intensive care unit data: A systematic review. Crit Care. 2019;23(1):284. doi:10.1186/s13054-019-2564-9. PubMed: 31439010. PMCID: PMC6704673.
- Obermeyer Z, Emanuel EJ. Predicting the future—Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219. doi:10.1056/NEJMp1606181. PubMed: 27682033. PMCID: PMC5070532.
| Volume | 04 | |
| Issue | 01 | |
| Received | 09/05/2026 | |
| Accepted | 12/05/2026 | |
| Published | 30/05/2026 | |
| Publication Time | 21 Days |