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基于机器学习的预测模型,用于怀孕晚期对分娩的恐惧

Xinxin Feng¹, Wenjing Yang¹, Siqi Wang¹

¹Xuzhou Medical University, Jiangsu Province, China.

Clinical nursing research

|September 10, 2025

概括

这项研究开发了机器学习模型,以预测孕妇对分娩的恐惧. 极端梯度增强 (XGB) 模型表现最好,有助于早期识别和干预.

科学领域:

医疗信息学医疗信息学
计算统计学计算统计学
孕产妇健康孕产妇健康

背景情况:

对分娩的恐惧 (FOC) 是影响母亲心理健康和分娩经历的一个重要问题.
早期识别和干预对于管理FOC和改善结果至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测怀孕后期怀孕期间孕妇的FOC风险.
确定与FOC相关的关键预测因素.

主要方法:

一项横截面的观察性研究包括406名孕妇.
六个ML算法 (拉索辅助后勤回归,随机森林,XGBoost,SVM,贝叶斯网络,KNN) 使用10倍交叉验证进行了评估.
预测因素包括疼痛灾难化,分娩期望,分娩偏好,药物使用和分娩应用程序使用.

主要成果:

XGBoost模型表现出卓越的性能,AUC为0.874,准确度为0.795,灵敏度为0.764,特异性为0.878.
拉索辅助后勤回归也显示出强的表现 (AUC=0.873).
确定了重要的预测因素,包括疼痛灾难化和对无痛分娩的期望.

结论:

ML模型,特别是XGBoost,可以有效地预测孕妇的FOC风险.
为临床应用而开发了一个基于逻辑回归的名图.
这些工具可以帮助医疗保健专业人员在早期发现和干预FOC.

关键词:

孩子的出生,就是分娩.恐惧恐惧恐惧恐惧机器学习是机器学习.产前护理产前护理风险预测风险预测

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