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Updated: Feb 4, 2026

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In Silico Clinical Trials for Cardiovascular Disease
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使用先进的区块链支持的健康监测来检测心血管疾病的Triboostcardio合奏模型
M Mayuranathan1, V Anitha2, P Nehru3
1Department of Computer Science and Engineering, SRM Valliammai Engineering College, Chengalpattu, Tamil Nadu, India.
Frontiers in artificial intelligence
|February 2, 2026
概括
这项研究介绍了使用物联网可穿戴设备和区块链进行安全数据管理的早期心脏病检测的先进模型. 该框架显著提高了对心血管疾病 (CVD) 的预测准确性和及时干预.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 心血管疾病 (CVD) 是全球死亡的主要原因,需要改进早期检测和管理策略.
- 传统的监控系统缺乏实时分析和个体洞察力,导致数字医疗保健中的干预延迟和数据隐私问题.
研究的目的:
- 使用物联网 (IoT) 可穿戴设备,电子健康记录和区块链技术开发早期心血管疾病 (CVD) 检测的综合模型.
- 提高心血管疾病检测的准确性和及时性,同时确保强大的数据隐私和安全性.
主要方法:
- 集成基于物联网的可穿戴设备,用于生理数据收集和电子临床记录.
- 应用卡尔曼过以提高数据可靠性,以及浅/深特征提取以识别模式.
- 使用折射砂猫群优化 (SCSO) 来实现功能最大化,并使用TriBoostCardio合奏模型 (CatBoost,AdaBoost,LogitBoost) 来进行分类.
- 实施区块链和智能合约,以实现对健康信息的安全和透明访问控制.
主要成果:
- 与传统方法相比,拟议的框架证明了心血管疾病早期检测的预测精度提高.
- 结合SCSO特征选择和TriBoostCardio Ensemble模型,显著提高了模型的稳定性和分类精度.
- 实验结果验证了该框架能够更早,更准确地检测心血管疾病的能力.
结论:
- 开发的框架为心血管疾病检测提供了安全,准确和及时的解决方案,解决了关键的数据隐私和完整性问题.
- 集成先进的优化技术,集体学习和区块链技术为现代医疗保健系统提供了可信的进步.
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