Smart Healthcare Monitoring System: Integrating IoT, Deep Learning, and XGBoost for Real-time Patient Diagnosis

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.