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使用机器学习预测早产:基于中国深大型学龄前儿童调查数据的综合分析

Liwen Ding¹, Xiaona Yin², Guomin Wen²

¹Department of Epidemiology and Health Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.

BMC pregnancy and childbirth

|December 5, 2024

概括

机器学习模型,特别是XGBoost,对预测早产 (PTB) 和识别多胎妊娠等关键风险因素充满希望. 这可以改善产前护理和公共卫生战略.

科学领域:

计算生物学和生物信息学
生殖健康和产科生殖健康和产科
数据科学和机器学习

背景情况:

早产 (PTB) 是新生儿死亡率和长期健康问题的主要原因.
准确的PTB预测对于降低儿童死亡率和发病率至关重要.
传统方法与复杂的,相互作用的风险因素作斗争.

研究的目的:

开发和评估六种用于PTB预测的机器学习 (ML) 模型.
通过大规模调查数据确定PTB的关键预测因素.
为了利用Shapley增量解释 (SHAP) 来解释模型的可解释性.

主要方法:

利用了来自84,050对母婴 (2021-2022) 的数据.
测试了六种ML模型:L1-LR,LightGBM,天真贝叶斯,RF,SVM和XGBoost.
评估了歧视,校准和临床实用性的模型,以SHAP为特征的重要性.

主要成果:

XGBoost获得了最好的性能 (AUC 0.752验证,0.757测试).
确定了关键预测因素:多胎妊娠,堕胎的威胁,母亲的年龄.
SHAP分析证实了多胎妊娠/堕胎威胁的积极影响和微量营养素补充的负面影响.

结论:

机器学习模型,特别是XGBoost,显示了准确PTB预测的巨大潜力.
确定关键的风险因素可以为个性化的产前护理策略提供信息.
研究结果支持使用ML来加强用于预防PTB的临床干预和公共卫生倡议.

关键词:

机器学习是机器学习.多重怀孕是多重怀孕的情况.预测模型的预测模型.过早分娩过早分娩是什么这就是 SHAP SHAP 的意思.受到堕胎威胁的堕胎

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