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HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
HealthFog: یک سیستم هوشمند درمانی هوشمند مبتنی بر یادگیری عمیق برای تشخیص خودکار بیماری های قلبی در محیط های IoT و محاسبات مه-2020 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. Keywords: Fog computing | Internet of things | Healthcare | Deep learning | Ensemble learning | Heart patient analysis |
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