Edge Health: A Decentralized, Privacy-Preserving Framework for Real-Time Illness Risk Prediction Using IoT and Machine Learning

Abstract

The Edge Health Framework offers an innovative solution to longstanding healthcare challenges, particularly in latency, scalability, and data privacy. By decentralizing health data processing through edge computing, leveraging IoT devices for real-time data collection, and employing machine learning models for predictive analysis at the edge, this framework provides a secure and efficient alternative to centralized healthcare systems. The research demonstrates that edge computing reduces latency by 85.9%, allowing for real-time patient monitoring and immediate responses to critical health conditions. Additionally, the framework scales efficiently, maintaining low latency and resource utilization even with up to 10,000 IoT devices. In terms of data security, the integration of encryption and differential privacy reduces exposure to data breaches by over 13x compared to traditional cloud-based systems. Furthermore, the framework's machine learning models show an increase in accuracy (94.5%) and recall (95.8%), providing reliable and precise illness risk predictions. These findings underscore the potential of the Edge Health Framework to transform healthcare systems, offering a scalable, privacy-preserving, and real-time solution that enhances patient outcomes and operational efficiency for healthcare providers and institutions.