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Fog-Based Smart Cardiovascular Disease Prediction System Powered by Modified Gated Recurrent Unit.

A Angel Nancy1, Dakshanamoorthy Ravindran1, Durai Raj Vincent2

  • 1Department of Computer Science, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli 620002, India.

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PubMed
Summary
This summary is machine-generated.

A novel fog-assisted smart healthcare system improves cardiovascular disease diagnosis using deep learning. This edge-fog-cloud model significantly reduces latency and enhances accuracy for time-critical medical applications.

Keywords:
Internet of Thingscardiovascular diseasecloud computingfog computinggated recurrent unithealthcareheart attackpredictive analyticsrecurrent neural network

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Area of Science:

  • * Computer Science, Artificial Intelligence, Healthcare Technology
  • * Focus on edge computing, fog computing, and cloud computing integration

Background:

  • * Cloud computing provides essential services but faces limitations like latency and bandwidth constraints with the Internet of Things (IoT).
  • * The proliferation of IoT devices generates massive data, exacerbating cloud-IoT interaction challenges.
  • * Fog computing offers a decentralized layer to mitigate these issues by bringing computation closer to data sources.

Purpose of the Study:

  • * To propose a fog-assisted smart healthcare system for diagnosing heart or cardiovascular disease.
  • * To leverage the edge-fog-cloud model for optimized data processing and predictive analytics in healthcare.
  • * To address latency, delay, and security vulnerabilities in time-critical healthcare applications.

Main Methods:

  • * Integration of a fuzzy inference system (FIS) for data pre-processing.
  • * Utilized the Gated Recurrent Unit (GRU), a variant of recurrent neural networks, for predictive analytics.
  • * Implemented a hierarchical edge-fog-cloud architecture with significant processing at the fog layer.

Main Results:

  • * Achieved a high classification accuracy of 99.125% for cardiovascular disease diagnosis.
  • * Demonstrated optimized results in latency, response time, and jitter compared to cloud-only solutions.
  • * Validated the effectiveness of deep learning models within the fog computing paradigm for healthcare.

Conclusions:

  • * The proposed fog-assisted system significantly enhances smart healthcare diagnostics.
  • * The edge-fog-cloud model effectively tackles massive data volumes and latency issues.
  • * Deep learning combined with fog computing offers near-perfect results for time-critical healthcare applications.