提高高血压风险诊断使用混合机器学习框架:利用身体组成数据
Abdul Wahid Mirzaye1, Hamid Saadatfar1, Mohammad Ali Nematollahi2
1Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran, birjand.ac.ir.
BioMed research international
|February 4, 2026
概括
这项研究引入了一种混合机器学习模型,用于使用身体组成预测高血压. 该框架通过提高准确性和可解释性来增强早期检测和个性化医疗保健.
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:
- 高血压是全球主要的健康风险,导致严重的心血管,脏和神经系统问题.
- 目前的预测方法往往缺乏可解释性和可靠性,阻碍了早期检测和个性化干预.
研究的目的:
- 开发和验证双情景混合机器学习框架,用于高血压风险预测.
- 通过使用非侵入性身体组成特征,提高高血压风险评估的解释性和预测可靠性.
主要方法:
- 场景1:对高血压患者进行无监督聚类 (K-Means),以确定生理子组,并通过聚类指数进行验证.
- 场景2:二进制分类 (ExtraTrees,KNN,SVM,GNB,决策树),比较标准和集群增强数据集.
主要成果:
- 场景1确定了五个不同的子组,在身体组成和年龄方面具有显著的集群间变异性 (p < 0.001),通过SVM.达到99.08%的准确性.
- 使用集群增强数据集的第二种情景显示,使用ExtraTrees分类器 (准确率98.23%,AUC99.87%) 的性能优越.
- 聚类和特征选择都改善了模型的概括性,特别是在集合方法中.
结论:
- 将无监督集群与监督分类相结合,为个性化的高血压风险预测提供了一个强大而可解释的框架.
- 拟议的混合模型通过提供更高的准确性和可解释性,为早期检测和精确医疗保健做出了贡献.
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