基于门诊血压监测和机器学习算法,高血压的智能风险分层
Muqing Deng1,2, Junsheng Guo1, Boyan Li1
1School of Automation, Guangdong University of Technology, Guangzhou, People's Republic of China.
Physiological measurement
|February 26, 2025
概括
这项研究引入了一种使用门诊血压监测和机器学习进行精确高血压风险分层的智能方法. 这种方法可以为心血管疾病提供早期警告.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 高血压风险分层对于临床治疗决策至关重要.
- 门诊血压监测 (ABPM) 数据提供了超越单个测量的动态洞察力.
- 调查高血压风险分层的ABPM数据仍然未得到充分探索.
研究的目的:
- 使用ABPM数据开发一种智能高血压风险分层方法.
- 利用改进的机器学习算法进行增强的风险评估.
- 为心血管疾病提供早期警告.
主要方法:
- 从ABPM数据中提取时间域,频域,非线性动态和相关性特征.
- 应用合成少数人过量采样技术 (SMOTE) 用于数据平衡.
- 使用粒子群集优化 (PSO) 与内核极端学习机器 (KELM) 进行特征融合和优化.
主要成果:
- 通过交叉验证实现了93.7%,97.8%和98.4%的高诊断准确率.
- 证明了直观和可量化的高血压风险分层.
- 验证了多维特征表示和学习的有效性.
结论:
- 拟议的智能方法有效地使用ABPM数据对高血压风险进行分层.
- 这种方法可以帮助早期检测潜在的心血管疾病.
- 这些发现支持先进机器学习在高血压管理中的临床实用性.
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