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Enhancing E-health system accuracy using Rendezvous Data Processing Model (RDPM) with IoT-cloud integration.

Sana Shahab1, Ashit Kumar Dutta2,3, Zaffar Ahmed Shaikh4,5

  • 1Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

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Summary
This summary is machine-generated.

The Rendezvous Data Processing Model (RDPM) enhances E-health monitoring by integrating IoT-cloud architecture, reducing errors and improving decision accuracy for better patient care.

Keywords:
E-health monitoring systemsInternet of Things-cloud rapportRDPMbig data analyticsintelligent systemsstate learning

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Area of Science:

  • Health Informatics
  • Internet of Things (IoT)
  • Cloud Computing

Background:

  • Current E-health systems often provide inaccurate or redundant recommendations due to reliance on static analytics and isolated data.
  • This limitation hinders the precision and performance of E-health monitoring systems.

Purpose of the Study:

  • To develop and implement a Rendezvous Data Processing Model (RDPM) compatible with IoT-cloud architecture.
  • To improve the precision and performance of E-health monitoring systems.

Main Methods:

  • The RDPM processes historical suggestions and current analytical flaws to enhance real-time decision-making.
  • It utilizes divided features and data streams for hypothesis validation against prior observations.
  • Internet-connected sensors collect patient and environmental data, with cloud analytics evaluating system precision.

Main Results:

  • RDPM demonstrated a reduction in data interruptions, analytical errors, and recommendation ratios.
  • The model improved decision correctness and accurately interpreted multiple input streams.
  • Compared to traditional IoT healthcare analytics, RDPM enhanced suggestion accuracy and reduced computational redundancy.

Conclusions:

  • The RDPM, integrated with IoT-cloud technologies, creates a scalable platform for advanced E-health monitoring.
  • State learning and dynamic data validation enable RDPM to provide more accurate and context-aware health recommendations.
  • This self-improving approach effectively manages massive, real-time datasets for healthcare systems.