Enhanced Framework for Predictive Analytics of Infants in Neonatal Integrating AI & IoT

Neonatal Intensive Care Units (NICUs) play a significant role in saving the lives of high-risk infants. However, conventional monitoring systems primarily focus on real-time tracking without integrating predictive analytics, leading to delays in identifying potentially life-threatening conditions such as jaundice, bronchopulmonary dysplasia, hydrocephalus, microcephaly, or respiratory failure. The proposed novel framework addresses the gap identified above by using noninvasive Internet of Things (IoT)-)-enabled devices with advanced machine learning models to monitor critical health metrics—such as heart rate, oxygen saturation, and body temperature—in real-time. The proposed architecture uses a hybrid Artificial Intelligence (AI) approach by combining Convolutional Neural Networks (CNN) with Gradient Boosting techniques, particularly XGBoost, to improve prediction performance and get around overfitting. This approach proactively detects possible health hazards by utilizing IoT sensor data. Compared to the existing techniques, the proposed model performs better, with an accuracy of 95.89% when tested on a dataset of neonatal conditions. The high efficiency of the proposed system offers promising potential for early diagnosis of neonatal conditions and improved health outcomes through intervention before serious complications arise. Neonatal care is handled proactively rather than reactively in this prototypical attempt toward innovative, technology-oriented health systems. Future works will focus on handling data privacy issues and scaling the dataset to optimize model interpretability in IoT-based healthcare systems.