在年轻人中预测牙科焦虑:经典的统计建模方法与机器学习方法
Chukwuebuka Ogwo1, Wisdom Osisioma2, David Ifeanyi Okoye3
1Department of Oral Health Sciences, Maurice H Kornberg School of Dentistry, Temple University, Philadelphia, PA, 19140, US. Chukwuebuka.ogwo@temple.edu.
BMC oral health
|March 9, 2024
概括
牙焦虑在年轻人中很普遍. 传统统计和机器学习都发现了类似的关键因素,有助于公共卫生干预减少对牙科的恐惧.
科学领域:
- 牙科公共卫生 牙科公共卫生
- 生物统计学 生物统计学
- 心理学 心理学 心理学
背景情况:
- 牙焦虑显著影响年轻成年人的口腔健康.
- 了解预测因素对于有针对性的干预至关重要.
研究的目的:
- 确定与年轻成年人牙科焦虑相关的人口和临床因素.
- 比较传统的统计建模与机器学习 (ML) 在预测这些因素方面的有效性.
主要方法:
- 涉及西伊利诺伊大学学生的横截面研究.
- 使用了社会人口统计问卷,修改牙科焦虑量表 (MDAS) 和牙科关注评估工具 (DCA).
- 将多重线性回归 (经典) 与LASSO回归和XGBoost (ML) 进行多变量分析.
主要成果:
- 平均MDAS得分为13.73±5.51,表明牙科焦虑的负担很高.
- 经典和ML方法都确定了12个类似的关键预测变量.
- 拉索回归显示出强大的预测性能,R2为0.615.
结论:
- 在年轻成年人中,牙焦虑的严重流行存在.
- 机器学习模型的预测能力与传统方法对牙科焦虑因素的预测能力相当.
- 识别的预测因素可以为公共卫生策略提供信息,以减轻牙科焦虑触发因素.
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