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使用机器学习预测成年人的负面自我评价口腔健康:巴西南部的一项纵向研究
Cinthia Fonseca Araujo1, Felipe Mendes Delpino2, Lílian Munhoz Figueiredo3
1Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil.
Journal of dentistry
|October 11, 2025
概括
机器学习模型在预测负面自我评价口腔健康 (SROH) 方面表现有前途. 需要进一步开发,以便在识别有风险的成年人时实际使用.
科学领域:
- 口腔健康研究 口腔健康研究
- 机器学习应用程序 机器学习应用程序
- 公共卫生 公共卫生
背景情况:
- 消极的口腔健康自我评价 (SROH) 是一个重要的公共卫生问题.
- 预测SROH对于开发有针对性的干预措施至关重要.
研究的目的:
- 开发和评估用于预测成年人负SROH的机器学习模型.
- 确定负SROH的关键预测因素.
主要方法:
- 从巴西南部的3,461名成年人的长度基于人口的调查数据.
- 分析了46个社会人口统计,行为,一般和口腔健康特征.
- 使用AUC-ROC,准确性,精度,回忆和F1评分对五种机器学习算法 (随机森林,LightGBM,CatBoost,XGBoost,TabPFN) 的评估.
主要成果:
- 16.6%的参与者报告了负的SROH.
- 机器学习模型的AUC-ROC得分在0.671和0.715之间,TabPFN表现最好.
- 关键预测因素包括社会经济地位 (ABEP指数),牙科服务类型,年龄,焦虑 (GAD-7) 和生活满意度.
结论:
- 机器学习模型在预测负SROH时表现出合理的性能.
- 为了实现现实世界的临床适用性,需要进一步细化.
- 这些模型可以帮助制定针对风险人群的有针对性的战略.
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