使用机器学习方法预测年轻成年人牙的结果
Chukwuebuka Ogwo1, Brown Grant2, John Warren3
1Temple University Kornberg School of Dentistry.
Research square
|October 27, 2023
概括
机器学习使用纵向数据准确地预测年轻成年人的牙损伤. 之前的空经验和含糖饮料摄入量是针对性干预措施的关键预测因素.
科学领域:
- 口腔健康研究 口腔健康研究
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 牙损仍然是一个重要的公共卫生问题,特别是在年轻人中.
- 预测病的发展需要了解各种风险和保护因素随时间的复杂相互作用.
研究的目的:
- 开发和评估机器学习模型,用于预测年轻成年人牙损伤的结果.
- 通过纵向数据识别出病发展的最重要的预测因素.
主要方法:
- 利用爱荷华化物研究数据集与纵向预测变量,包括人口统计,社会经济地位,过去的病经验和生活方式因素.
- 我们比较了四种机器学习模型:LASSO回归,通用提升机 (GBM),负二项式 (NegGLM) 和使用交叉验证的极端梯度提升 (XGBOOST).
主要成果:
- 拉索回归表现出最好的表现,具有很高的确定系数 (R2=0.44) 和准确度 (83.7%),可以预测.
- 在13岁和17岁之前的空经历,以及在同一个年龄段摄入含糖饮料被确定为最有影响力的预测因素.
- 该模型实现了高精度 (85.9%) 和回忆 (93.1%) 在分类腐烂风险.
结论:
- 机器学习模型可以使用纵向数据准确地预测年轻成年人的.
- 已识别的预测因子,如先前的皮和饮料摄入量,可以为有针对性的公共卫生干预提供信息.
- 为了提高模型的通用性和临床实用性,需要对不同人群进行进一步的验证.
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