PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION

D Viswasahithya; B.SHIVARAM; K.DIVYA TEJA; B.VINEELA; B. SIREESHA; V. TILAK

PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION

April 2025

Vol-11, Issue-2

Paper ID: 26124

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

INFORMATION TECHNOLOGY

Keywords

Heart failure prediction newborn healthcare early diagnosis machine learning decision tree random forest logistic regression XGBoost medical AI neonatal care healthcare automation clinical decision support.

Abstract

The early detection of heart failure in newborn babies is critical to improving their health outcomes. Newborns are vulnerable to various life-threatening conditions, including heart failure, which can often go undiagnosed due to the subtle nature of early symptoms. Timely detection and intervention are essential for reducing mortality rates and enhancing the quality of care. However, traditional methods of diagnosis can be slow and inefficient, making it crucial to explore machine learning as a tool for automating the detection process. This project seeks to bridge the gap by utilizing machine learning algorithms—specifically Decision Tree, Random Forest, Logistic Regression, and XGBoost—to predict heart failure in newborns. By applying these models, healthcare professionals can make quicker and more accurate decisions. The motivation behind this project is to leverage advanced technologies to support doctors in their efforts to provide optimal care for newborns, ultimately leading to better health outcomes, reduced complications, and lower healthcare costs.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	D Viswasahithya	SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY
2	B.SHIVARAM	SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY
3	K.DIVYA TEJA	SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY
4	B.VINEELA	SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY
5	B. SIREESHA	SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY
6	V. TILAK	SIDDHARTH INSTITUTE OF ENGINEERING & TECHNOLOGY

How to Cite

Use the following formats to cite this article in your research.

APA Style

Viswasahithya, D, B.SHIVARAM, TEJA, K.DIVYA, B.VINEELA, SIREESHA, B., & TILAK, V. (2025). PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 1350-1357.

MLA Style

Viswasahithya, D, et al. "PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 1350-1357.

IEEE Style

D Viswasahithya, B.SHIVARAM, K.DIVYA TEJA, B.VINEELA, B. SIREESHA, and V. TILAK, "PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 1350-1357, 2025.

Vancouver Style

Viswasahithya D, B.SHIVARAM, TEJA K.DIVYA, B.VINEELA, SIREESHA B., TILAK V.. PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):1350-1357.

Harvard Style

Viswasahithya, D, B.SHIVARAM, TEJA, K.DIVYA, B.VINEELA, SIREESHA, B., & TILAK, V. (2025) 'PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 1350-1357.

Chicago Style

Viswasahithya, D, et al. "PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 1350-1357.

Turabian Style

Viswasahithya, D, et al. "PREDICTING NEONATAL CARDIAC ARREST IN THE CICU: A STATISTICAL MACHINE LEARNING APPROACH FOR EARLY INTERVENTION." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 1350-1357.