使用机器学习方法预测年轻人牙损伤的结果
Chukwuebuka Ogwo1, Grant Brown2, John Warren3
1Department of Oral Health Sciences, Temple University Maurice H Kornberg School of Dentistry, 3223 N Broad Street, L216, Philadelphia, PA, 19131, US. Chukwuebuka.ogwo@temple.edu.
BMC oral health
|May 3, 2024
概括
机器学习准确地预测了年轻人的牙损伤,使用过去的牙损伤经验和含糖饮料的摄入量作为关键预测因素. 这种方法可以帮助确定有针对性的公共卫生干预的风险人群.
科学领域:
- 口腔健康 口腔健康
- 数据科学数据科学数据科学
- 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.
- 预测建模为早期识别和干预提供了一个有希望的途径.
- 纵向数据分析对于了解疾病进展和风险因素至关重要.
研究的目的:
- 用纵向数据预测年轻成年人牙损伤的结果.
- 通过机器学习技术,识别病发展的关键预测因素.
- 为了评估不同的机器学习模型在虫预测方面的表现.
主要方法:
- 利用爱荷华化物研究数据集与纵向预测变量.
- 通过5倍交叉验证对 LASSO 回归,GBM,NegGLM 和 XGBOOST 模型进行比较.
- 包括人口,社会经济,化物,饮食和行为因素作为预测因素.
主要成果:
- 拉索回归显示出卓越的性能,R2为0.44和RMSE为0.70.
- 分类准确度,精度和回忆率分别达到83.7%,85.9%和93.1%.
- 过去的空经验 (年龄13-17岁) 和含糖饮料摄入量 (年龄13-17岁) 是最强的预测因素.
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.
- 确定关键预测因素可以为有针对性的公共卫生查和干预提供信息.
- 建议对多种不同人群进行进一步验证,以提高可通用性.
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