基于学习的多式联体方法,用于早期检测和个性化治疗心脏病
Sultan Alasmari1,2, Rayed AlGhamdi3, Ghanshyam G Tejani4,5
1Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia.
Frontiers in physiology
|May 8, 2025
概括
这项研究引入了一种新的框架,用于使用多式联网数据和联合学习来准确检测心脏病. 这种方法确保了隐私,并实现了高的诊断准确性,为更好的心脏健康管理铺平了道路.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 心脏病是全球主要的死亡原因.
- 目前的诊断方法由于数据整合的挑战而缺乏准确性.
- 早期检测对于有效的心脏病管理至关重要.
研究的目的:
- 开发一个保护隐私的框架,以准确检测心脏病.
- 整合异质的健康数据,以提高诊断能力.
- 为了实现早期诊断和个性化的生活方式建议.
主要方法:
- 使用多式联络数据分析框架,整合心脏图像,心电图信号,患者记录和营养数据.
- 采用基于注意力的特征融合模型进行数据集成.
- 实现了与本地训练深度神经网络 (SGD-DNN) 的联合学习,以进行保护隐私的分类.
主要成果:
- 在心脏病检测方面取得了高准确性:97.76%在数据库1,98.43%在数据库2和99.12%在数据库3.
- 在多个数据集中证明了拟议框架的稳定性和通用性.
- 验证了多模式特征融合和联合学习在心脏诊断方面的有效性.
结论:
- 拟议的框架为心脏病管理提供了一个可扩展的,以隐私为中心的解决方案.
- 允许早期诊断和个性化的生活方式建议,同时确保数据保密.
- 显示出在心血管护理中对现实世界的临床实施有很大的潜力.
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