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An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations.

Reyazur Rashid Irshad1, Ahmed Abdu Alattab1, Omar Ali Saleh Alsaiari1

  • 1Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Najran, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a secure IoT healthcare system using homomorphic encryption and a novel CCRBM-WO algorithm for disease detection. The system enhances patient data privacy and achieves high accuracy in forecasting diseases from remote patient data.

Keywords:
Centered Convolutional Restricted Boltzmann Machineshomomorphic encryptionsmart healthcarewhale optimization algorithm

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Area of Science:

  • Internet of Things (IoT) in Healthcare
  • Cybersecurity in Medical Systems
  • Computational Intelligence for Health Monitoring

Background:

  • IoT-enabled healthcare apps offer cost-effective patient monitoring but face significant security and privacy challenges due to sensitive data.
  • Traditional methods struggle with processing large datasets common in e-healthcare systems.
  • Ensuring patient data privacy and security is paramount in cloud-based healthcare environments.

Purpose of the Study:

  • To propose a novel, secure healthcare monitoring system for tracking and forecasting diseases using patient data from remote communities.
  • To address the challenges of data security, privacy, and efficient processing in IoT-enabled healthcare applications.
  • To enhance the accuracy and reliability of e-healthcare solutions through advanced computational methods.

Main Methods:

  • A three-stage framework: IoT sensor-based data collection, homomorphic encryption (HE) for secure storage, and disease detection using a Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm.
  • Utilized a Python-based cloud tool for experimental implementation and validation.
  • Employed advanced computational intelligence techniques for efficient data categorization and disease prediction.

Main Results:

  • The proposed system demonstrated superior performance compared to existing e-healthcare solutions.
  • Achieved high performance metrics: 96.87% accuracy, 97.45% precision, 97.78% F1-measure, and 98.57% recall.
  • Successfully validated the effectiveness of the CCRBM-WO algorithm in disease detection within a secure IoT framework.

Conclusions:

  • The developed IoT healthcare system effectively balances patient monitoring with robust data security and privacy using homomorphic encryption.
  • The CCRBM-WO algorithm provides an efficient and accurate method for disease detection from large, complex datasets.
  • This research offers a promising advancement in secure and intelligent remote patient monitoring systems.