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量子启发的启发式算法用于安全的医疗预测,使用区块链技术.

Hirak Mazumdar, Chinmay Chakraborty, Satheesh Bojja Venkatakrishnan

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    一个新的量子启发的启发式算法与克里尔群优化 (QKHO) 结合,提高了医疗预测的准确性和安全性. 这种方法提高了患者数据隐私和传输效率,优于传统模型.

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    科学领域:

    • 医疗信息学 医疗信息学
    • 医疗保健中的人工智能
    • 医学领域的网络安全

    背景情况:

    • 环境变化和营养不良对公众健康产生负面影响,增加了对健康意识的需求.
    • 医疗保健系统面临着诸如数据不足,安全威胁,错误和传输延迟等挑战.
    • 推进医疗保健需要创新的解决方案,以确保安全和高效的数据处理.

    研究的目的:

    • 提出一种安全的医疗预测方法,优先考虑患者隐私和数据传输效率.
    • 引入量子启发的启发式算法与克里尔群优化 (QKHO) 结合用于医疗预测.
    • 评估QKHO模型与传统方法的性能,并评估其安全性.

    主要方法:

    • 开发和实施量子启发的启发式算法与克里尔群群优化 (QKHO) 相结合.
    • 对QKHO与使用Krill Herd Optimization (KHO) 优化的深前进神经网络 (DFNN) 的比较分析.
    • 集成区块链技术,以安全地将数据传输到服务器.

    主要成果:

    • 与传统模型相比,拟议的QKHO模型表现出优越的性能.
    • 在医疗预测中,QKHO实现了更高的准确性,精度,回忆和F1得分.
    • 区块链技术为数据传输提供了增强的安全性,超过了RSA和Diffie-Hellman算法.

    结论:

    • QKHO模型在安全和准确的医疗预测方面取得了重大进展.
    • 区块链技术的整合确保了医疗保健系统中强大的数据安全性和隐私.
    • 这种方法解决了医疗保健数据管理和预测方面的关键挑战.