基于机器学习的高血压发展促成因素分析:一项比较研究
Marenao Tanaka1,2, Yukinori Akiyama3, Kazuma Mori1,4
1Department of Cardiovascular, Renal and Metabolic Medicine, Sapporo Medical University School of Medicine, Sapporo, Japan.
Clinical and experimental hypertension (New York, N.Y. : 1993)
|January 8, 2025
概括
机器学习模型准确地预测新出现的高血压,使用静缩血压,年龄和脂肪肝指数 (FLI). 这些模型为高血压预测提供了一个实用的方法,有进一步发展的潜力.
科学领域:
- 心血管疾病的研究研究.
- 生物统计学和机器学习应用程序
- 公共卫生和预防医学.
背景情况:
- 高血压是一个重要的全球健康问题.
- 使用纵向数据对高血压的预测建模尚未得到充分探索.
- 机器学习 (ML) 为复杂的健康数据提供了先进的分析能力.
研究的目的:
- 调查各种ML模型对新发性高血压的预测性能.
- 从大型数据集中确定高血压发展的关键预测因素.
- 评估ML在高血压风险评估中的有效性.
主要方法:
- 利用了来自15965名接受年度健康检查的日本参与者的纵向数据.
- 采用了五种ML模型:逻辑回归,随机森林,天真贝叶斯,极端梯度增强和人工神经网络.
- 评估了58个候选预测指标,包括脂肪肝指数 (FLI),使用接收机运行特征曲线分析.
主要成果:
- 系统性血压,年龄和FLI通过随机森林特征选择被确定为高血压的显著预测因素.
- ML模型实现了曲线下的面积 (AUC) 值,范围从0.765到0.825.
- 人工神经网络模型与物流回归相比,显示出更高的区分能力.
结论:
- ML模型有效地预测高血压的发展,使用缩血压,年龄和FLI.
- 这些发现支持使用ML进行简单而准确的高血压预测.
- 开发多个ML模型可能会提高高血压风险预测的实用性.
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