Smart Healthcare Monitoring System: Integrating IoT, Deep Learning, and XGBoost for Real-time Patient Diagnosis
Kruthika Paulraj, Nisha Soms, S. David Samuel Azariya, S. Sathya Priya, J. Jeba Emilyn, Vidhushavarshini Sureshkumar
Abstract
Integrating the Internet of Things (IoT), Deep Learning (DL), and the XGBoost algorithm has paved the way for transformative advancements in healthcare. This paper presents a pioneering study on a "Smart Healthcare Monitoring System" that harnesses the synergy of these technologies for real-time patient diagnosis. With the escalating demand for accurate and swift medical assessments, our work addresses the crucial need for timely and precise diagnosis. The proposed system amalgamates IoT-enabled wearable devices and sensors to capture comprehensive patient data. A hybrid approach is employed to leverage this data, comprising a Deep Learning model for intricate pattern recognition and the XGBoost algorithm for rapid decision-making. Nonetheless, challenges such as data security and model interpretability are acknowledged, highlighting avenues for future research. This paper underscores the transformative potential of the IoT-Deep Learning-XGBoost integration, offering a robust foundation for further innovation in intelligent healthcare systems. As healthcare delivery evolves, our work underscores the promise of this interdisciplinary fusion in revolutionizing patient diagnosis and care.