开发一个可解释的机器学习框架来预测和分析2至6岁儿童的早期:一个单中心的观察性研究
Xinyue Yuan1, Yiting Chu1, Wenyan Cai1
1Department of Stomatology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
International dental journal
|February 6, 2026
概括
幼儿 (ECC) 是一个常见的问题. 机器学习模型将母亲的教育,饮食和生活方式确定为关键预测因素,使得有针对性的预防策略能够改善儿童口腔健康.
科学领域:
- 儿科牙科 儿科牙科
- 公共卫生 公共卫生
- 医疗保健中的机器学习
背景情况:
- 幼儿 (ECC) 在全球范围内对儿童的整体健康和生活质量产生重大影响.
- 虽然已知ECC的众多风险因素,但预测因素的系统优先级是有限的.
- 了解关键决定因素对于有效的干预策略至关重要.
研究的目的:
- 通过使用先进的分析方法,识别和优先考虑早期儿童 (ECC) 的预测因素.
- 开发和评估用于预测儿童ECC风险的机器学习 (ML) 模型.
- 为了实现针对性预防干预的风险分层.
主要方法:
- 一项涉及900名儿童的观察性研究,收集有关人口统计,父母背景,养,健康,卫生和饮食的数据.
- 单变量和多变量分析以确定与ECC相关的因素.
- 开发和比较五个ML模型 (LR,RF,DT,KNN,SVM) 用于ECC预测和风险分层.
主要成果:
- 发现的关键预测因素包括年龄,母亲的教育,维生素补充,碳水化合物摄入量,夜间食,睡眠照明和户外活动.
- 后勤回归 (LR) 显示出最高的预测准确率为99.4%,其次是SVM (98.9%) 和RF (96.7%).
- ML模型成功地将儿童分为ECC风险低,中等和高的群体.
结论:
- 机器学习模型有效地确定了关键的ECC决定因素,强调了母亲的教育,饮食和生活方式.
- 基于ML预测的风险分层可以指导有针对性的早期预防性干预.
- 这些发现支持通过明智的策略来减少儿童早期病的全球负担.
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