Remote Patient Health Monitoring Frameworks using IoT and ML: A Comparative Study
Keywords:
Internet of Things, Machine Learning, patient-centric, data-driven model, Remote Patient Health MonitoringAbstract
The convergence of Internet of Things (IoT) and Machine Learning (ML) technologies has opened new avenues in the field of healthcare, offering innovative solutions for remote patient health monitoring. This novel approach promises to revolutionize healthcare by enabling continuous and real-time monitoring of patients' health status, allowing for early detection of anomalies and timely interventions. In this paper, it is explored the synergy between IoT and ML in the context of remote patient health monitoring, emphasizing its potential to enhance healthcare outcomes, reduce healthcare costs, and improve overall patient well-being. This research work also addresses the challenges and ethical considerations associated with implementing such systems. As healthcare evolves towards a more patient-centric and data-driven model, the integration of IoT and ML stands as a promising paradigm shift, fostering personalized and proactive healthcare solutions for patients worldwide.
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