Wang Haiyan, Yin Yanxing, Wang Liu, Wang Yifan, Liu Xiaotong, Shi Lijuan
College of Computer Science and Technology, Changchun University, Changchun 130022, China.
College of Electronic Information Engineering, Changchun University, Changchun 130012, China.
BioTech (Basel). 2025 Jul 28;14(3):57. doi: 10.3390/biotech14030057.
Maternal and child health during pregnancy is an important issue in global public health, and the classification accuracy of fetal cardiotocography (CTG), as a key tool for monitoring fetal health during pregnancy, is directly related to the effectiveness of early diagnosis and intervention. Due to the serious category imbalance problem of CTG data, traditional models find it challenging to take into account a small number of categories of samples, increasing the risk of leakage and misdiagnosis. To solve this problem, this paper proposes a two-step innovation: firstly, we design a method of adaptive adjustment of misclassification loss function weights (MAAL), which dynamically identifies and increases the focus on misclassified samples based on misclassification rates. Secondly, a primary and secondary correction network model (MAC-NET) is constructed to carry out secondary correction for the misclassified samples of the primary model. Experimental results show that the method proposed in this paper achieves 99.39% accuracy on the UCI publicly available fetal health dataset, and also obtains excellent performance on other domain imbalance datasets. This demonstrates that the model is not only effective in alleviating the problem of category imbalance, but also has very high clinical utility.
孕期母婴健康是全球公共卫生领域的重要问题，而胎儿心动图（CTG）作为孕期监测胎儿健康的关键工具，其分类准确率直接关系到早期诊断和干预的效果。由于CTG数据存在严重的类别不平衡问题，传统模型难以兼顾少数类别的样本，增加了漏诊和误诊的风险。为解决这一问题，本文提出了两步创新：首先，设计了一种误分类损失函数权重自适应调整方法（MAAL），基于误分类率动态识别并增加对误分类样本的关注。其次，构建了主次校正网络模型（MAC-NET），对主模型的误分类样本进行二次校正。实验结果表明，本文提出的方法在UCI公开可用的胎儿健康数据集上准确率达到99.39%，在其他领域不平衡数据集上也取得了优异的性能。这表明该模型不仅能有效缓解类别不平衡问题，还具有很高的临床实用性。
