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
IEEE journal of biomedical and health informatics
|August 11, 2023
概括
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.
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.
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