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提前预测妊娠前:一个定制的机器学习管道,集成重新采样和组合模型来处理不平衡的医疗数据

Yinyao Ma¹, Hanlin Lv², Yanhua Ma¹

¹Department of Obstetrics, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, China.

|March 25, 2025

概括

这项研究开发了一个优化的管道,用于在不平衡的数据集中预测妊娠前. 最好的模型使用了反向加权高斯混合模型重新采样和梯度增强决策树,提高了早期检测的准确性.

科学领域:

医疗信息学医学信息学
机器学习在医疗保健中的应用
临床预测建模临床预测建模

背景情况:

在不平衡的医疗数据集中进行预测建模,比如孕前症,会带来重大挑战.
在这种情况下,集成机器学习算法进一步复杂化了模型开发.

研究的目的:

开发一个强大的管道,以提高整体机器学习模型的性能.
使用不平衡的数据集,实现预先预测子前期.

主要方法:

建立了一个全面的管道,使用电子健康记录和公共数据集进行验证.
进行了对重新抽样技术,少数与多数的比率和整体算法的系统评估.
分析了超过4,600个模型配置,使用G-mean,MCC,AP和AUC等指标,并使用先进的统计测试进行微调.

主要成果:

系统的变量优化显著影响了预测性能.
最佳配置涉及反向加权高斯混合模型重新采样,梯度提升决策树,以及0.09的少数与多数比率.
这种配置实现了0.6694的几何平均值,在所有指标上表现优于基线模型.

结论:

建立了一个强大的管道,在不平衡的数据中显著提高了孕前的预测模型性能.
战略变量优化对于医学诊断至关重要,在其他不平衡的阶级场景中具有潜在的应用.

关键词:

阶级不平衡造成的不平衡整合机器学习机器学习预测模型的预测模型.孕前症孕前症重新采样技术重新采样技术

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