使用机器学习方法预测与不受控制的高血压相关的因素:伊朗西部的一项横截面研究
Zahra Cheraghi1,2, Mahboobeh Doosti-Irani3, Masoumeh Sohrabi2
1Department of Epidemiology, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Institute of Health Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran.
International journal of hypertension
|February 27, 2025
概括
失控的高血压是一个全球性的健康问题. 这项研究发现,身体活动较低,健康素养较低,不坚持治疗显著影响高血压控制,突出了干预的关键领域.
科学领域:
- 心脏病学 心脏病学
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 失控的高血压仍然是一个重要的全球公共卫生挑战.
- 有效的管理需要了解促成因素.
- 机器学习为分析复杂的健康数据提供了新的方法.
研究的目的:
- 使用机器学习识别与不受控制的高血压相关的关键因素.
- 评估不同机器学习模型的预测性能.
主要方法:
- 一项对303名患有高血压的成年人进行的横截面研究.
- 通过标准健康素养问卷收集的数据.
- 使用后勤回归分析,通过精度,AUC和变量重要性来评估性能.
主要成果:
- 逻辑回归实现了75.4%的准确性和0.87.7的AUC.
- 失控高血压的关键预测因素包括体力活动较低,健康素养较低和不坚持治疗.
- 高盐摄入与不受控制的高血压相反相关.
结论:
- 坚持治疗,身体活动,健康素养和盐摄入量对于高血压控制至关重要.
- 针对这些可修改因素的干预措施可以帮助管理和预防不受控制的高血压.
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