卫生工作者与健康相关的生活质量:使用回归,条件树和森林进行比较分析
Fatemeh Rezaei Chegini1, Mozhgan Seif2,3, Mohebat Vali2,3
1Student Research Committee, Shiraz University of Medical Science, Shiraz, Iran.
BMC public health
|December 24, 2025
概括
机器学习确定了影响医疗保健工作者的健康相关生活质量 (HRQoL) 的关键因素. 睡眠质量,潜在疾病,性别和教育显著影响HCW福祉,指导有针对性的干预措施.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 医疗保健工作者 (HCW) 对社会健康至关重要.
- 了解影响HCW健康相关生活质量 (HRQoL) 的因素至关重要.
- 机器学习为分析复杂的健康决定因素提供了新的方法.
研究的目的:
- 调查影响HCWHRQoL的因素.
- 应用和比较机器学习算法来识别HRQoL决定因素.
- 为旨在改善HCW福祉的干预提供信息.
主要方法:
- 基于Shiraz医学科学大学员工队列 (SUMSEC) 数据的横截面,基于人群的研究 (n=7073).
- 应用了多重线性回归,条件树,条件森林和随机森林算法.
- 使用MSE,RMSE,R2和Pearson相关性评估模型性能.
主要成果:
- 多重线性回归和条件森林显示出卓越的预测性能.
- 这些模型显示了更高的相关性和R2值,MSE和RMSE较低.
- HCW HRQoL的关键决定因素包括睡眠质量,潜在疾病,性别和教育.
结论:
- 在这个分析中,多重线性回归和条件森林同样有效.
- 人口,临床和社会经济因素显著影响HCW HRQoL.
- 针对特定因素的有针对性的干预措施可以提高HCW的性和福祉.
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