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

By Shreshth Tuli, Nipam Basumatary, Sukhpal Singh Gill, Mohsen Kahani, Rajesh Chand Arya, Gurpreet Singh Wander, Rajkumar Buyya. Future generations computer systems, 2019

567 citations recorded by the source corpus.

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 which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

Subfields: Biomedical Engineering, Computer Networks and Communications, Health Information Management

View Paper: https://www.semanticscholar.org/paper/f7ca07c4cbc9c9affb620dd9762b582d3b203121

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shreshth Tuli
2. Manmeet Singh
3. Sukhpal Singh Gill
4. Shikhar Tuli

In Health Information Management

1. Yogesh Kumar
2. Ruchika Singla
3. Apeksha Koul
4. Muhammad Fazal Ijaz

Citations in the corpus

• Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges (2019, background): “With the massively growing number of IoT devices [74] and the increasing data collected, the data-handling capacity, computation capability and bandwidth requirements of networks are being pushed to its limits.”
• Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, background): “Tuli et al. (2019) proposed a Health fog framework to integrate deep learning in edge computing devices and incorporate it into the real-”
• IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “The authors in [150] present a fog based system using a deep neural network to detect heart disease from a patients vital signs (blood oxygen, heart rate, respiration rate, EEG, ECG, EMG, blood pressure, glucose) and activity data.”