泰国人口中严重牙周炎风险预测模型的开发:统计和机器学习方法
Htun Teza1, Anuchate Pattanateepapon1, Attawood Lertpimonchai2,3
1Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
JMIR formative research
|December 14, 2023
概括
与机器学习方法相比,混合效应后勤回归 (MELR) 模型在查严重牙周炎风险方面表现优越. 这种统计模型为识别需要牙周检查的人提供了更有效的工具.
科学领域:
- 牙周病学和牙科公共卫生
- 生物统计学和医疗保健中的机器学习
背景情况:
- 严重的牙周炎是一种常见的疾病,导致膜骨损失,需要有效的查方法,而不仅仅是耗时的全口腔检查.
- 开发一个有针对性的查模型可以优化牙科工作量,并识别高风险个人及时干预.
研究的目的:
- 为了比较严重牙周炎风险的统计 (混合效应物流回归) 和机器学习模型的预测性能.
- 评估不同建模方法对严重牙周炎大规模查的有用性.
主要方法:
- 利用泰国成年人的前性队列数据,包括人口统计,临床和实验室特征.
- 开发并比较了使用混合效应逻辑回归 (MELR),循环神经网络,混合效应支持矢量机器和混合效应决策树的风险预测模型.
- 严重的牙周炎是使用既定的临床附着水平和牙周口袋深度标准来定义的.
主要成果:
- MELR模型实现了0.983的高曲线下面积 (AUC) 和11.9的正概率比 (LR+).
- 机器学习模型,包括循环神经网络,混合效应支持矢量机器和混合效应决策树,表现出明显较低的性能 (AUCs从0.662到0.712不等).
- 最终的MELR模型确定了关键预测特征:性别,教育,吸烟,糖尿病,牙数量和斑块得分.
结论:
- 与经过测试的机器学习模型相比,MELR模型在严重牙周炎风险的大规模查方面具有优越的实用性.
- 建议进行进一步的外部验证,以确认MELR模型在不同人群和环境中的通用性.
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