预测青少年对性身份问题的不响应 风险行为监测:对复杂调查数据的机器学习分析
Yu He1, Chanapong Rojanaworarit2
1Mailman School of Public Health, Columbia University, New York, NY, USA.
Annals of epidemiology
|February 1, 2026
概括
机器学习模型在青年风险行为监测系统 (YRBSS) 中准确地预测了对敏感性身份问题的不响应. 随机森林模型表现最好,识别了脆弱年轻人没有反应的关键风险因素.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 调查方法 调查方法
背景情况:
- 在像青年风险行为监测系统 (YRBSS) 这样的调查中,未回答敏感问题可能会导致结果偏差.
- 了解不响应的预测因素对于准确的数据收集和分析至关重要.
研究的目的:
- 将七个机器学习 (ML) 模型进行比较,用于预测2023年YRBSS中对性认同问题的非响应.
- 确定表现最佳的ML模型和与非响应相关的关键属性.
主要方法:
- 分析了20103名学生的数据,其中包括32个个人特征,学校行为,物质使用和性活动的预测因素.
- 开发和比较监督的ML模型 (随机森林,梯度增强等). ) 纳入调查权重.
- 使用F1分数,AUC和AUPRC评估模型性能.
主要成果:
- 大约10%的学生没有回答性别认同问题,少数群体的比例更高.
- 随机森林 (RF) 模型在所有评估指标中表现最强.
- 不响应的关键预测因素包括由于安全问题而缺席学校,以及拥有多个性伴侣.
结论:
- 对性认同问题的不回答是非随机的,不成比例地影响了弱势学生群体.
- 机器学习模型在非响应方面取得了强大的预测性能.
- 对其他敏感问题的响应模式显著影响不响应,影响调查设计和调整策略.
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