孕期体重增加的预测建模:一项机器学习多类分类研究
Audêncio Victor1, Hellen Geremias Dos Santos2, Gabriel Ferreira Santos Silva3
1School of Public Health, University of São Paulo (USP), Avenida Doutor Arnaldo, 715, São Paulo, 01246904, São Paulo, Brazil. audenciovictor@gmail.com.
BMC pregnancy and childbirth
|November 8, 2024
概括
机器学习准确地预测妊娠期体重增加 (GWG) 类别,早期识别有风险的怀孕. 这有助于个性化产前护理,以获得更好的孕产妇和胎儿健康结果.
科学领域:
- 孕产妇和胎儿的医学
- 计算生物学是一种计算生物学.
- 公共卫生 公共卫生
背景情况:
- 孕期体重增加 (GWG) 显著影响母亲和胎儿的健康.
- 偏离推的GWG与妊娠糖尿病,高血压和不良出生结果等并发症有关.
- 预测GWG类别对于及时干预至关重要.
研究的目的:
- 开发和评估用于预测GWG类别的机器学习 (ML) 模型 (低于,在或高于推指南).
- 确定影响妊娠期体重增加的关键预测因素.
- 评估ML在提高产前护理以获得最佳妊娠结果方面的实用性.
主要方法:
- 分析了巴西阿拉库拉拉群组1557名孕妇的数据.
- 利用社会经济,人口,生活方式,发病率和人类学因素作为预测因素.
- 采用并比较了五种ML算法:随机森林,LightGBM,AdaBoost,CatBoost和XGBoost用于多类分类.
主要成果:
- 在XGBoost模型中,GWG在推范围内的AUC-ROC为0.79的AUC-ROC表现最高.
- 发现的关键预测因素包括妊娠前的BMI,孕妇年龄,血糖概况,血红蛋白水平和手臂周长.
- 在GWG类别的分布是:在 (28.7%),以下 (32.5%) 和以上 (38.7%) 建议.
结论:
- 机器学习模型提供了一种可靠的方法来预测GWG类别,从而能够早期识别有风险的怀孕.
- 这种预测能力支持个性化的产前护理和有针对性的干预措施.
- 该研究强调了ML的潜力,通过加强妊娠管理来改善孕产妇和胎儿健康结果.
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