new born babies

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.

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.