HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments (1911.06633v1)

Abstract: Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy-efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes that provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

Analysis of HealthFog: A Smart Healthcare System for Heart Disease Diagnosis in IoT and Fog Computing Environments

The paper "HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments" investigates the integration of fog computing, edge computing, and IoT technologies to improve the efficiency and accuracy of heart disease diagnosis. The research proposes HealthFog, a framework which leverages the computational benefits of fog computing, coupled with deep learning, to deliver timely and precise healthcare services for heart patients.

Overview and Methodology

The work moves beyond traditional cloud frameworks which, due to inherent latency issues and dependency on centralized data centers, are not well-suited to cater to real-time processing requirements of IoT-based applications. HealthFog addresses this by deploying ensemble deep learning models directly at the edge, i.e., fog devices, closer to data sources, thereby enhancing data processing efficiency and reducing latency. Deploying healthcare services on a fog environment using the FogBus framework is key, permitting real-time data analytics with minimal energy consumption and latency.

HealthFog's architecture is robust, combining hardware components like body area sensor networks and gateways, and software modules that include data pre-processing, resource management, and deep learning-based prediction capabilities. The framework achieves low latency and high accuracy by employing a blend of data pre-processing algorithms and ensemble deep learning techniques—namely, bagging of neural networks—which improves both prediction accuracy and confidence.

Numerical and Performance Insights

The system's performance was evaluated with the Cleveland heart disease dataset, focusing on key metrics such as accuracy, response time, network bandwidth usage, and power consumption. Noteworthy outcomes highlighted in the paper include:

• Prediction Accuracy: HealthFog achieves a test accuracy upward of 94% with ensemble models, significantly surpassing isolated models. This increase in accuracy with ensemble learning underscores the utility of bagging to enhance decision reliability by mitigating overfitting on modest-sized datasets.
• Efficiency: By localizing computation at the fog layer, HealthFog demonstrated reduced response time compared to cloud-centric architectures. This enabled quick data processing and delivered results to healthcare providers in real-time, a critical necessity for managing heart diseases.
• Resource Utilization: The paper underscores a tactical distribution of computational loads between edge nodes and cloud servers to optimize power efficiency—important for IoT applications constrained by battery limitations.

Implications and Future Prospects

The research offers significant implications for improving healthcare delivery via decentralized computing structures. The fusion of fog computing with deep learning presents a promising avenue for IoT-driven applications where immediacy and result fidelity are paramount. It suggests a transformative potential for healthcare standardization processes that may currently depend on human expertise.

However, the integration of such systems faces challenges, such as establishing data integrity and security. Future prospects of HealthFog include expanding its scope to other healthcare applications (e.g., diabetes and cancer diagnosis), optimizing cost efficiency, and enhancing adaptability to other edge-computing use cases.

In conclusion, HealthFog exemplifies how fog computing paradigms, tethered with state-of-the-art machine learning techniques, can be engineered to meet the dual objectives of high precision and low latency in health diagnosis. This offers vivid pathways to reimagining the future of real-time healthcare solutions that can adapt dynamically to user and application demands.