对大学生睡眠质量预测模型的新见解:一种LASSO衍生睡眠评估模型
Ling Yao1,2, Qingquan Chen3, Kang Yang4
1The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
Frontiers in psychiatry
|May 12, 2025
概括
这项研究使用机器学习开发了大学生睡眠质量的预测模型. 该模型确定了影响睡眠的关键因素,有助于更好地管理校园健康.
科学领域:
- 睡眠科学 睡眠科学
- 机器学习应用 机器学习应用
- 公共卫生 公共卫生
背景情况:
- 睡眠障碍对大学生来说是一个重大问题,它会影响他们的精神和身体健康.
- 现有的研究缺乏大学生睡眠质量的全面预测模型.
研究的目的:
- 开发和验证大学生睡眠质量的预测模型.
- 为了确定这一群体中睡眠障碍的关键预测因素.
主要方法:
- 通过使用匹兹堡睡眠质量指数 (PSQI) 和定制问卷,对20645名大学生的数据进行分析.
- 应用LASSO回归用于变量选择和人工神经网络 (ANN) 用于模型开发.
- 使用培训测试分割 (70%/30%) 验证,并与其他机器学习技术进行比较.
主要成果:
- 确定了睡眠障碍的八个关键预测因素:年龄,专业,呼吸史,咖啡消费,夜,长时间在线活动,突然变化和不耐烦的闭环管理.
- 该ANN模型表现出卓越的性能,AUC为0.713.
- 在研究人口中,睡眠障碍的患病率为28.9%.
结论:
- 一个验证的大学生睡眠障碍预测模型是使用LASSO回归和ANN开发的.
- 该模型作为预测睡眠质量的实用工具,并为校园健康战略提供信息.
- 这些发现支持了针对大学生改善睡眠的有针对性的干预措施的必要性.
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