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基于主辅助校正网络的自适应动态权重的胎儿健康诊断

Haiyan Wang¹, Yanxing Yin¹, Liu Wang¹

¹College of Computer Science and Technology, Changchun University, Changchun 130022, China.

Biotech (Basel (Switzerland))

|August 22, 2025

概括

这项研究引入了一种新的方法,通过解决类别失衡来改善胎儿心脏图谱 (CTG) 分析. 这种新方法提高了诊断准确度,

科学领域:

医疗信息学
公共卫生
生物医学工程

背景情况:

母婴健康是全球公共卫生的优先事项.
胎儿心脏图 (CTG) 对于监测胎儿在怀孕期间的健康状况至关重要.
在CTG数据中的类别不平衡对传统诊断模型构成挑战,可能导致错误诊断.

研究的目的:

开发一种创新的方法来提高胎儿CTG数据的分类准确性.
解决CTG数据集中的类别不平衡问题.
提高对胎儿健康的早期诊断和干预的有效性.

主要方法:

设计了一种适应性调整错误分类损失函数权重 (MAAL) 的方法,以关注代表性不足的样本.
开发了一种初级和二级校正网络模型 (MAC-NET),用于重新评估错误分类的样本.
使用UCI公开可用的胎儿健康数据集进行验证.

主要成果:

在UCI胎儿健康数据集上达到99.39%的准确性.
在其他不平衡的域数据集上表现出色.
有效地缓解了CTG数据中的类别不平衡问题.

结论:

拟议的MAAL和MAC-NET模型显著提高了CTG分类的准确性.
这种方法为处理胎儿健康监测不平衡数据提供了可靠的解决方案.
该模型对改善母亲和儿童健康结果具有很高的临床效用.

关键词:

适应性加权心脏图谱 (CTG)数据类别不平衡胎儿健康问题二次纠正

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