机器学习对一所大学员工的高血压查计划进行了五年时间的评估
Olumide Adeleke1,2, Segun Adebayo3, Halleluyah Aworinde4
1Directorate of Health Services, Bowen University, Iwo, Nigeria. olumide.adeleke@bowen.edu.ng.
Scientific reports
|December 5, 2024
概括
使用机器学习的职场高血压查是有效的. 这项研究发现,高血压在40岁以上的员工中很常见,但从2018年到2022年,患病率下降了,这表明机器学习.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
背景情况:
- 全球高血压患病率很高,特别是在低收入和中等收入国家.
- 工作场所为早期高血压诊断和治疗提供了一个实用的环境.
- 机器学习方法在职场健康评估中未得到充分利用.
研究的目的:
- 通过机器学习评估高血压的职场查策略.
- 为了分析高血压趋势的员工健康检查数据.
- 评估使用k-means集群用于在劳动力中检测高血压的可行性.
主要方法:
- 利用了一所大学1723名员工的匿名数据集.
- 分析数据包括人口统计,血压,年龄组,部门和性别.
- 应用机器学习,特别是k-means集群,以确定血压状态 (低,正常,高).
主要成果:
- 劳动力平均年龄为42岁.
- 高血压在40岁以上的员工中普遍存在,不论性别或工作类型.
- 从2018年到2022年,观察到高血压患病率的持续下降.
结论:
- 机器学习是定期监测工作场所健康状况的可行和可持续工具.
- 这种方法可以帮助在劳动力中诊断和控制高血压.
- 与机器学习相结合的工作场所查策略显示出对公共卫生干预措施的前景.
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