机器学习和传统的基于后勤回归的预测模型在种族多样化的人口中对妊娠糖尿病进行比较;莫纳什GDM机器学习模型
Yitayeh Belsti1, Lisa Moran2, Lan Du3
1Monash Centre for Health Research and Implementation (MCHRI), Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia; University of Gondar, College of Medicine and Health Science, Ethiopia.
International journal of medical informatics
|September 29, 2023
概括
机器学习模型显著改善了孕妇妊娠糖尿病 (GDM) 的预测. CatBoost 分类器表现出卓越的性能,增强了及时干预的早期风险识别.
科学领域:
- 产科和妇科 产科和妇科
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 早期识别高患妊娠糖尿病 (GDM) 风险的孕妇对于实施有效的生活方式干预至关重要.
- 可以使用统计或机器学习方法开发个性化的GDM风险预测模型,但准确性需要改进.
研究的目的:
- 为了比较各种机器学习算法,开发GDM风险预测模型.
- 确定最佳的机器学习模型来预测孕妇GDM.
主要方法:
- 监督机器学习预测分析利用了从2016年1月到2021年6月的常规产前护理数据.
- 预测集包括既有变量 (例如BMI,种族,年龄) 和新的,更强大的连续和衍生变量.
- 在80%的数据上开发了模型,在20%的数据上进行了验证,使用校准,歧视指标和决策曲线分析来评估性能.
主要成果:
- 机器学习和物流回归模型实现了曲线下的面积 (AUC) 在71%和93%之间.
- CatBoost分类器 (CBC) 显示了最高的预测性能.
- 在预测器组4上进行的CBC检测,达到85%的准确率,90%的精度和78%的回忆率.
结论:
- 机器学习方法在预测GDM方面明显优于传统的统计方法,性能从75%提高到93%.
- 包括连续变量在内的CatBoost分类器成为GDM风险预测中最有效的模型.
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