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Updated: Sep 9, 2025

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机器学习可以防止死产:能否将数据转化为拯救生命的洞察力?

Maria Eduarda Ferro de Mello¹, Élisson da Silva Rocha¹, Patricia Takako Endo²

¹Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Pernambuco, Brazil.

BMC pregnancy and childbirth

|September 1, 2025

概括

机器学习模型使用巴西的孕产妇健康数据预测胎儿死亡. 主要预测因素包括产前护理,年龄和教育,重点是技术

科学领域:

母亲健康
机器学习
公共卫生

背景情况:

预测胎儿死亡对于母婴健康至关重要.
使用社会人口统计和健康史数据可以提高预测准确性.

研究的目的:

对预测胎儿死亡的机器学习模型的性能进行评估.
比较数据归算技术和平衡场景.
在巴西人口中确定胎儿死亡的关键预测因素.

主要方法:

采用了来自巴西珀南布科 (Pernambuco) 的社会项目数据集 (2008-2022年).
使用随机低采样 (RU) 和混合低采样2x (H2X) 的平衡技术.
评估了四种基于树的机器学习模型 (例如,XGBoost,随机森林) 的性能和特征重要性.

主要成果:

XGBoost获得了81.06%的特异性; 随机森林获得了67.73%的敏感性.
第一次产前护理,母亲的年龄,教育程度和怀孕间隔是重要的预测因素.
在不同的平衡场景和归算技术中,模型的性能有所不同.

结论:

机器学习为预测胎儿死亡提供了宝贵的工具.
这种方法可以支持巴西的社会项目, 并有助于实现可持续发展目标.
将技术纳入医疗保健可以产生重大社会影响.

关键词:

平衡技术机器学习死亡出生

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