开发基于机器学习的风险预测模型来预测婴儿快速体重增加:对七个队列的分析
Miaobing Zheng1,2, Yuxin Zhang1, Rachel A Laws1
1Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia.
JMIR public health and surveillance
|June 18, 2025
概括
机器学习模型现在可以预测婴儿的快速体重增加 (RWG),这是未来肥胖风险的关键指标. 这些工具为及时干预和改善儿童健康结果提供了早期识别.
科学领域:
- 儿科健康 儿科健康
- 机器学习应用 机器学习应用
- 预防肥胖 预防肥胖
背景情况:
- 婴儿期的快速体重增加 (RWG) 是后期肥胖的一个重要预测因素.
- 婴儿RWG的早期识别可以及时评估肥胖风险.
- 在婴儿体重增长图表上,RWG被定义为跨百分线的上升转变.
研究的目的:
- 开发和验证机器学习 (ML) 风险预测模型,以在1岁之前识别婴儿RWG.
- 为了利用常规收集的产前和产后早期数据进行风险预测.
- 评估各种ML算法在预测婴儿RWG方面的性能.
主要方法:
- 来自7个澳大利亚和新西兰队列 (n=5233) 的数据被用于模型开发和验证.
- 训练了8个ML算法,使用诸如母亲怀孕前体重,吸烟,妊娠年龄,平价,婴儿性别,出生体重,母乳养和固体引入时间等因素来预测RWG.
- 数据被分为培训 (70%) 和测试 (30%) 组,用于模型一致性评估的5倍交叉验证.
主要成果:
- 婴儿RWG的平均患病率为27%.
- ML模型证明可接受的优异歧视,ROC曲线下的面积 (AUC) 在训练集中从0.75到0.86不等.
- 梯度增强模型显示出最好的预测准确性,在测试组中的验证显示出对真实阳性 (准确度和灵敏度>0.75) 的良好的预测能力.
结论:
- 该研究成功开发了第一个基于ML的婴儿RWG风险预测模型,其准确度可接受.
- 这些模型可以整合到常规儿童成长监测系统中.
- 这些模型有可能在初级医疗保健机构促进全民早期肥胖风险评估.
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