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

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.