Integration of Biomedical Sensor Data and Machine Learning for Energy-Efficient Smart Healthcare Networks

The combination of biomedical sensor technologies with advanced machine learning techniques marks a significant advancement in developing sustainable, energy-efficient smart healthcare systems. As hospitals increasingly rely on digital infrastructures, the rising demand for real-time patient monitoring and data analytics has substantially increased energy consumption. This study proposes a conceptual framework integrating biomedical Internet of Things (IoT) sensors, cloud-based learning architectures, and adaptive energy management strategies to enhance operational efficiency and environmental sustainability. The presented model features three layers-patient-centric data acquisition, machine learning-driven diagnosis, and energy optimization-designed to reduce redundant power usage across interconnected devices. Simulations under various patient and energy scenarios demonstrate that the framework can boost energy efficiency by ۳۰-۴۰% while maintaining diagnostic accuracy above ۹۵%. These results confirm that merging biomedical informatics with intelligent energy systems provides a viable pathway to creating green, patient-focused smart hospital environments.

کلیدواژه ها:

Smart Healthcare Networks ، Machine Learning ، Biomedical Internet of Things (BIOT) ، Energy-Efficient Computing ، Edge-Cloud Architecture ، Reinforcement Learning ، Deep Learning ، Cyber-Physical Systems Intelligent ، Wireless Sensor Networks (WSNs)