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Author :
Yoheswari S
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Page No: 700 - 708
Abstract : The integration and security of healthcare data are crucial challenges in the modern healthcare system, driven by the increasing digitization of medical records, diagnostic data, and patient health information. The need for secure, interoperable, and efficient data management systems has become essential, especially as healthcare providers strive to offer better care and reduce operational inefficiencies. Traditional systems often suffer from issues like data breaches, lack of interoperability, and inefficient data handling processes, resulting in compromised data integrity and patient privacy. This paper proposes an optimized blockchain-enabled solution for secure data integration in healthcare, utilizing machine learning (ML) and genetic algorithms to enhance data management efficiency. Blockchain technology is inherently secure, providing decentralized, tamper-proof, and transparent records of data transactions. Coupling this technology with machine learning enables real-time analysis of healthcare data, which can improve patient outcomes and operational decision-making. The genetic algorithm is incorporated as an optimization technique to streamline the blockchain's performance by minimizing latency, energy consumption, and transaction time while maintaining security and data integrity. The proposed system leverages blockchain's decentralized architecture for data security, while machine learning algorithms analyze patient health data to detect patterns, predict outcomes, and optimize healthcare delivery. The genetic algorithm further optimizes the performance of blockchain-based processes, including smart contract execution, consensus mechanisms, and resource allocation. By combining blockchain, machine learning, and optimization techniques, this solution addresses critical challenges in data integration and security, offering a scalable and robust system for healthcare providers. Our experimental results demonstrate improved data security, optimized transaction speeds, and enhanced data integration capabilities. This approach fosters interoperability among different healthcare systems while safeguarding patient privacy, making it a viable solution for the future of secure healthcare data management.
Keyword: Blockchain, healthcare data integration, machine learning, genetic algorithm, optimization, secure data management, smart contracts, data security, interoperability, patient privacy.
Reference:

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