[基于机器学习的预测模型,用于苏州9岁儿童第一个牙的虫]
Lingzhi Chen1, Xiaqin Wang1, Kaifei Zhu2
1Dept. of Dentistry, Suzhou Wuzhong People's Hospital, Suzhou 215128, China.
概括
机器学习模型准确地预测了儿童的第一个永久性虫病. 高糖饮食和口腔卫生不良是儿童牙腐烂的关键风险因素.
科学领域:
- 儿科牙科 儿科牙科
- 医疗保健中的机器学习
- 公共卫生 公共卫生
背景情况:
- 儿童牙,特别是第一个永久性牙,对口腔健康构成重大挑战.
- 早期识别脏病风险因素对于儿童群体的有效预防策略至关重要.
研究的目的:
- 使用机器学习算法,开发一个预测模型,用于9岁儿童的第一个永久性虫.
- 为了确定与此年龄段虫病发病率相关的重大风险因素.
主要方法:
- 来自苏州的7225名9岁学生的队列接受了口头考试和调查.
- 五个机器学习模型 (随机森林,决策树,XGBoost,物流回归,LightGBM) 进行了训练和验证.
- 使用沙普利增量解释 (SHAP) 来确定风险因素对预测模型的贡献.
主要成果:
- 第一个永久性牙的整体牙率为54.96%.
- 经常饮用含糖饮料,甜点和零食,特别是在睡前,与脏病风险增加有显著的关联 (P<0.05).
- XGBoost模型实现了最高的预测准确度,曲线下的面积 (AUC) 为90.1%.
结论:
- 极端梯度增强 (XGBoost) 算法在预测儿童第一个永久性虫病方面表现出高效.
- 糖果摄入量高和口腔卫生习惯差被确定为关键风险因素,突出了干预目标.
- 这些发现支持制定有针对性的公共卫生干预措施,以缓解儿童牙损伤.
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