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Updated: Feb 14, 2026

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解决胎儿健康分类中的阶级不平衡:对心脏病学数据的多类重新采样方法的严格基准测试.
Zainab Subhi Mahmood Hawrami1, Mehmet Ali Cengiz2, Emre Dünder3
1Ministry of Higher Education and Scientific Research-KRG, Kirkuk Main Road, Erbil 44001, Kurdistan Region, Iraq.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
这项研究使用心脏图谱 (CTG) 数据对胎儿健康分类的重新采样策略进行了基准测试. 战略性过量采样,特别是随机森林的BSMOTE,显著改善了罕见病理病例的检测,提高了胎儿健康查的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 生物医学应用的信号处理.
背景情况:
- 通过心电图 (CTG) 监测胎儿健康对于产前护理结果至关重要.
- 手动CTG的解释在考官之间存在很大的差异.
- 在CTG数据集中的类不平衡 (<10%的病理病例) 阻碍了对关键条件的准确检测.
研究的目的:
- 系统地比较五个重新采样策略与七个分类器家族进行多类CTG分类.
- 为了评估使用不平衡感知指标超出整体准确度的表现.
- 建立可靠的,模型不可知的基线,用于自动化胎儿健康查.
主要方法:
- 评估了七个机器学习模型 (例如,随机森林,SVM,MLP).
- 五种重新抽样方法 (SMOTE,BSMOTE,ADASYN,NearMiss,SCUT) 与原来的不平衡数据集进行了比较.
- 使用平衡精度 (BACC),宏F1,宏MCC和宏平均ROC-AUC来评估性能.
主要成果:
- 随机森林 (RF) 成为最可靠的模型.
- 带有BSMOTE的RF在类平衡中取得了最强的表现 (宏-MCC = 0.8533,宏-F1 = 0.9073).
- 过量采样技术,特别是SMOTE和BSMOTE,显著改善了少数群体的阶级歧视和跨模型的平衡指标.
结论:
- 战略性过量抽样有效地提高了CTG分析中少数群体类别的检测.
- 这些发现为部署机器学习在胎儿健康查中提供了强大的基线.
- 改善的歧视转化为临床上有意义的性能增长在成本敏感的设置.
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