HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

• Published in: Future generation computer systems Vol. 104; pp. 187 - 200
• Main Authors: Tuli, Shreshth, Basumatary, Nipam, Gill, Sukhpal Singh, Kahani, Mohsen, Arya, Rajesh Chand, Wander, Gurpreet Singh, Buyya, Rajkumar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020
• Subjects: Deep learning; Ensemble learning; Fog computing; Healthcare; Heart patient analysis; Internet of things; Fog computing; Deep learning; Internet of things; Healthcare; Ensemble learning; Heart patient analysis
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2019.10.043

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. •HealthFog is a real-life healthcare application platform for heart patients•HealthFog integrates ensemble deep learning with Edge computing.•HealthFog analyzes and identifies the Heart Diseases automatically.•HealthFog delivers diverse healthcare configurations for different user requirements.•HealthFog efficiently manages the data of heart patients.•HealthFog optimizes performance parameters and deployed using FogBus.