先进的机器学习在第一季度妊娠糖尿病中没有超越传统的后勤回归 预测:来自中国东部的一项回顾性单中心研究
Hongyan Ni1, Jinli Miao2, Jian Chen3
1Department of maternity care, PingHu Maternal and Child Health Hospital, Jiaxing, Zhejiang, 314200, People's Republic of China.
International journal of general medicine
|May 2, 2025
概括
后勤回归 (LR) 优于先进的机器学习 (ML) 模型在使用早期怀孕数据预测妊娠糖尿病 (GDM). 传统模型可能为GDM识别提供更高的有效性.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 孕期糖尿病 (GDM) 具有显著的孕产妇和胎儿健康风险.
- 目前用于GDM的诊断工具缺乏足够的有效性.
- 早期识别和干预对于管理GDM至关重要.
研究的目的:
- 使用早期怀孕数据开发和比较GDM的预测模型.
- 评估传统的逻辑回归 (LR) 与六种机器学习 (ML) 模型的性能.
- 在中国队列中确定早期GDM预测最有效的模型.
主要方法:
- 追溯分析956名孕妇的医学检查数据.
- 使用13个已识别的特征开发LR和ML模型.
- 使用ROC曲线,精度回忆曲线,DCA,校准曲线和HL测试来评估模型性能.
主要成果:
- LR模型实现了0.787的最高曲线下面积 (AUC),超过了ML模型 (例如,随机森林0.776).
- 该LR模型证明了良好的校准和临床实用性.
- 特征选择涉及斯皮尔曼相关性和Boruta算法.
结论:
- 与GDM的先进ML模型相比,传统LR模型显示出更高的预测性能.
- 简单的传统模型可能比复杂的ML方法更有效,用于早期GDM预测.
- 研究结果表明,物流回归是早期GDM识别的潜在更有效工具.
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