IoT-Enabled Low-Cost Fog Computing System with Online Machine Learning for Accurate and Low-Latency Heart Monitoring in Rural Healthcare Settings

TL;DR

This paper proposes a low-cost IoT-enabled fog computing system with online machine learning for accurate, low-latency heart monitoring in rural healthcare, aiming to reduce costs and improve early detection of cardiovascular issues.

Contribution

It introduces a novel architecture combining fog and cloud computing with online machine learning for ECG analysis in rural healthcare settings.

Findings

Potential to reduce healthcare costs by up to 80%

Improved accuracy and low-latency in heart monitoring

Enhanced early detection of cardiovascular issues

Abstract

Healthcare services in rural areas face numerous challenges due to the high cost of treatment and a lack of appropriate services. The application of Internet of Things (IoT) technology has shown potential in mitigating these issues. This article discusses the potential of Internet of Things (IoT) and fog computing to reduce healthcare costs and improve patient outcomes. The use of these technologies in cardiovascular health informatics is explored, along with the economic thought process of hospital decision-makers and end-of-life practices in intensive care units. Remote monitoring using IoT devices is highlighted as a promising way to detect health issues before they become serious, leading to earlier interventions and improved health outcomes. The use of fog computing in healthcare is also discussed, with a focus on its ability to provide real-time data processing, analysis, and decision-making capabilities. The article presents a novel architecture for Device-as-a-Service, utilizing both fog and cloud computing to improve the efficiency and accuracy of ECG device processing, and concludes that it has the potential to reduce costs by up to 80% in the Iranian market. The adoption of fog computing in healthcare is acknowledged to present significant challenges, such as security and privacy concerns,