机器学习可以预测青少年未经治疗的牙
1School of Dentistry, Federal University of Mato Grosso do Sul, Campo Grande, Brazil. aiello.rafael@gmail.com.
BMC oral health
|March 9, 2024
概括
机器学习准确地预测了未经治疗的牙损伤的青少年. XGBoost算法确定了牙线习惯和饮食等关键因素,使得更早的干预能够获得更好的口腔健康结果.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 儿科牙科 儿科牙科
背景情况:
- 未经治疗的牙损伤对青少年来说是一个重大的公共卫生挑战.
- 早期预测和干预对于管理青少年口腔健康至关重要.
- 机器学习为识别风险人群提供了新的方法.
研究的目的:
- 开发和评估机器学习模型,用于预测青少年未经治疗的牙损伤.
- 在巴西青少年群体中确定与未经治疗的牙相关的关键预测因素.
- 探索人工智能在优化家庭健康团队工作流程的实用性,用于牙损伤管理.
主要方法:
- 利用了来自巴西南部马托格罗索州615名青少年的流行病学调查数据.
- 采用了三个机器学习算法:XGBoost,决策树和后勤回归.
- 使用社会人口统计数据,饮食习惯和口腔卫生行为训练和测试预测模型.
主要成果:
- XGBoost表现出优异的性能,曲线下的面积 (AUC) 为84%,超过了决策树 (81%).
- 未经治疗的牙损伤的重要预测因素包括牙线使用,不健康的食物消费,自我宣称的种族和暴露在化水中.
- 这些模型成功地根据八个主要预测变量识别了未经治疗的牙患者.
结论:
- 人工智能,特别是机器学习,可以有效地预测未经治疗的牙的青少年.
- 家庭健康团队可以利用这些人工智能工具来增强他们的工作流程.
- 早期预测可以促进及时的牙科预约和治疗,改善青少年的口腔健康结果.
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