将逻辑回归和机器学习用于肥胖风险预测进行比较:系统性审查和元分析
Nancy Fosua Boakye1, Ciarán Courtney O'Toole2, Amirhossein Jalali2
1Research Ireland Centre for Research Training in Foundations of Data Science, Department of Mathematics and Statistics, University of Limerick, Ireland; Health Research Institute (HRI), University of Limerick, Limerick, V94T9PX, Ireland.
International journal of medical informatics
|March 29, 2025
概括
机器学习 (ML) 和逻辑回归 (LR) 在预测肥胖风险方面表现相似. 进一步的研究应该集中在改善研究报告和验证,以便更好地预测健康结果.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 逻辑回归 (LR) 是对二进制健康结果预测的传统方法.
- 机器学习 (ML) 方法越来越多地用于健康结果预测.
研究的目的:
- 将ML和LR对肥胖风险的预测性能进行比较.
- 识别肥胖预测研究中常见的比较方法和流行的ML技术.
主要方法:
- 在主要的科学数据库 (PubMed,Scopus,Embase,IEEE Xplore,Web of Science) 中进行全面的文献搜索.
- 用曲线下的面积 (AUC) 来量化预测性能的元分析.
- 对LR和表现最好的ML模型的AUC的比较.
主要成果:
- 包括28项研究,其中14项为分析做出了贡献.
- 聚合LR的AUC为0.75,ML为0.76,表明没有显著差异.
- 决策树和提升算法是最常见的ML方法;精度和灵敏度是经常使用的性能指标.
结论:
- ML和LR在肥胖风险预测方面表现相似.
- 需要加强研究报告,包括校准措施和在不同人群中验证.
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