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从母体血清生物标志物早期预测早产的比较机器学习模型

Kaleem Maqsood¹, Javeria Malik², Mahnoor Fatima³

¹Department of Biology, Lahore Garrison University, Lahore, Pakistan. Kaleemmaqsood4@gmail.com.

BMC pregnancy and childbirth

|February 23, 2026

概括

孕产妇的炎症和高BMI是早产 (PTB) 的关键预测因素. 使用这些因素的物流回归模型为PTB提供了可靠的早期风险评估.

科学领域:

产科和妇科产科和妇科
生物医学信息学生物医学信息学
生殖健康生殖健康

背景情况:

过早分娩 (PTB) 显著导致新生儿发病率和死亡率.
炎症和代谢干扰与PTB的病理学有关.

研究的目的:

评估母体血清炎症和脂质标志物作为PTB的预测因素.
为了比较各种机器学习模型在预测PTB方面的有效性.

主要方法:

招募了孕妇,将186人分为PTB和140人分为预产期分娩组.
使用T测试进行参数变化分析,并通过热图可视化Pearson相关性.
开发并验证了随机森林,物流回归,XGBoost和SVM模型,使用70/30分割和5倍交叉验证.

主要成果:

CRP,IL-6,C3,BMI和脂质与PTB呈正相关性;HDL呈反相关性.
多变量逻辑回归确定了年龄,BMI,IL-6,C3和CRP作为独立的预测因素.
所有机器学习模型都表现出强烈的歧视 (测试AUC≥0.819),后勤回归实现了最高的准确性 (78.57%) 和AUC (0.849).

结论:

孕产妇的炎症和BMI升高是PTB的重要危险因素.
综合临床和血清标志物的物流回归模型提供了有效的PTB风险评估.
这种可解释的模型有助于在临床环境中对PTB的早期风险分层.

关键词:

生物标志物生物标志物炎症炎症是一种炎症.机器学习是机器学习.过早分娩过早分娩是什么

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