公共卫生信息学中的机器学习:证据表明,对于具有不平衡结果的预测模型,可能不需要复杂的采样结构
Zhengye Si1, Jinpu Li1, Emily Leary1
1Department of Orthopaedic Surgery, Thompson Laboratory for Regenerative Orthopaedics, School of Medicine, University of Missouri, 1100 Virginia Avenue, Columbia , MO 65211, USA.
Annals of epidemiology
|January 11, 2025
概括
机器学习模型的预测能力与使用美国国家健康和营养检查调查数据的骨关节炎预测传统方法相似,没有采样重量.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 国家卫生调查往往有不平衡的结果数据,这给预测建模带来了挑战.
- 传统的统计模型可能无法优化处理复杂的调查设计和不平衡的数据.
- 机器学习为分析大规模健康调查数据提供了替代方法.
研究的目的:
- 评估机器学习模型在国家卫生调查中不平衡结果的预测性能.
- 将机器学习模型与传统的逻辑回归进行比较,其中包含复杂的采样设计.
- 在不使用调查采样权重的情况下评估模型.
主要方法:
- 使用了美国国家健康和营养检查调查 (USNHANES) 数据.
- 比较支持向量机器,随机森林,LASSO回归和深度神经网络.
- 采用过量采样,不足采样和组合重新采样技术来解决阶级不平衡.
- 与复杂的抽样设计集成的物流回归进行基准测试.
主要成果:
- 机器学习模型表现出与传统方法相比的平衡精度 (0.72-0.76).
- 支持向量机器和神经网络在灵敏度 (0.79-0.83) 中表现出色.
- 随机森林实现了最高的特异性 (0.86-0.96).
- 曲线下面积 (PR-AUC) 和Brier分数表明模型性能不同.
结论:
- 机器学习模型的预测能力与使用USNHANES数据预测骨关节炎的既定方法相似.
- 评估的机器学习模型可以有效地预测不平衡的结果,而无需采样权重.
- 这些发现支持在分析复杂的国家卫生调查数据时使用机器学习.
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