区块链辅助的心脏病检测的比较研究,使用基于机器学习的方法,并扩展了数据集
Mohammad Rifat Ahmed1, Abdul Aziz1, Md Motaleb Hossen Manik1
1Department of Computer Science and Engineering, Khulna University of Engineering &, Technology, Khulna 9203, Bangladesh.
Computers in biology and medicine
|June 28, 2025
概括
这项研究引入了一种使用机器学习和区块链技术检测心血管疾病 (CVD) 的新系统. 综合方法实现了89.2%的准确性,提高了早期检测和心脏健康数据安全性.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 心血管疾病 (CVD) 是全球死亡的主要原因,每年占所有死亡人数的31%.
- 显然,在心血管健康方面,急需先进的检测和安全的数据管理.
- 当前的挑战包括准确的预测和保护敏感的患者信息.
研究的目的:
- 开发一个完整的系统,以准确检测心血管疾病.
- 通过加密和区块链增强医疗数据的安全性,透明度和完整性.
- 通过提高诊断能力,减轻心血管疾病的影响.
主要方法:
- 整合机器学习模型 (决策树,随机森林,naive bayes,knn,神经网络) 用于疾病预测.
- 实施一个强大的加密算法,以确保数据安全.
- 利用私有区块链框架进行防改存储和数据完整性.
主要成果:
- 投票组合技术与各种机器学习模型相结合,在心血管疾病检测中实现了89.2%的准确性.
- 该系统成功地将预测建模与安全,分散的数据存储集成在一起.
- 证明了心血管健康应用的增强准确性和数据安全性.
结论:
- 拟议的系统在心血管疾病的检测和管理方面取得了重大进展.
- 机器学习和区块链的结合为医疗数据提供了一个安全而准确的平台.
- 实施承诺改善患者的治疗结果和更有效地管理与心血管疾病相关的挑战.
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