Recent Advancements in Health Monitoring and Early Warning Systems using IoT and Machine Learning

Abstract

The integration of Internet of Things (IoT) sensors and Machine Learning (ML) algorithms in healthcare has significantly advanced health monitoring and early warning systems. These technologies enable real-time patient surveillance, predictive diagnostics, and proactive medical interventions, thereby mitigating the risks associated with chronic diseases, postoperative care, and emergency responses. However, challenges persist regarding data privacy, interoperability, latency, and computational efficiency. This review explores various IoT architectures, ML methodologies, and Fog Computing frameworks, with a particular focus on the HealthFog system. Existing studies, such as the FogCepCare model and low-cost health monitoring (LCHM) framework, demonstrate the efficacy of fog-based IoT healthcare solutions but highlight concerns related to execution time, accuracy, and Quality of Service (QoS) metrics. Additionally, advancements in ensemble learning and federated learning have been evaluated for their roles in improving predictive accuracy and real-time healthcare analytics. This study underscores the transformative potential of IoT-ML-based frameworks, while addressing the need for improved security protocols, standardization, and cost-effective deployment strategies to ensure sustainable and scalable smart healthcare solutions.

Keywords: IoT in Healthcare, Machine Learning, Health Monitoring Systems, Fog Computing, Early Warning Systems, Predictive Analytics, Edge AI, HealthFog, Data Privacy, Real-time Health Surveillance