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A Machine-Learning-Blockchain-Based Authentication Using Smart Contracts for an IoHT System.

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Summary
This summary is machine-generated.

This study introduces a machine learning and blockchain system for secure, efficient medical data management. It enhances the Internet of Health Things (IoHT) system

Keywords:
CP-ABEIoHTML-basedSVMblockchainsecure systemsmart contracttraining set

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Area of Science:

  • Computer Science
  • Medical Informatics
  • Cryptography

Background:

  • Centralized medical data storage presents significant challenges.
  • Distributed cloud-based systems offer improved data accessibility.
  • Blockchain technology provides secure, decentralized transaction management.

Purpose of the Study:

  • To propose an integrated machine learning and blockchain system for medical data.
  • To enhance security, reduce resource consumption, and build trust in real-time medical applications.
  • To improve the accuracy and computational performance of Internet of Health Things (IoHT) systems.

Main Methods:

  • Development of a machine-learning-blockchain-based smart-contract system.
  • Integration of machine learning algorithms with blockchain technology for data processing and storage.
  • Implementation within an Internet of Health Things (IoHT) framework.

Main Results:

  • The proposed system significantly enhances security for medical data.
  • It leads to reduced resource consumption compared to traditional systems.
  • The system demonstrates improved accuracy and computation performance for IoHT.

Conclusions:

  • The machine-learning-blockchain smart-contract system offers a secure and trustworthy solution for real-time medical data management.
  • Integration of machine learning and blockchain optimizes data processing and storage.
  • This approach effectively addresses key challenges in medical data handling and treatment efficacy determination.