Gu Xiaochen, Huang Ping, Xu Xiaohua, Zheng Zhicheng, Luo Kaiju, Xu Yujie, Jia Yizhen, Zhou Yongjin
Eye Hospital, Wenzhou Medical University, Wenzhou, Zheijang, 325027, China.
School of Biomedical Engineering, Medical School, Shenzhen University, Shenzen, Guangdong, 518058, China.
Vis Comput Ind Biomed Art. 2024 Aug 27;7(1):22. doi: 10.1186/s42492-024-00172-9.
Fetal macrosomia is associated with maternal and newborn complications due to incorrect fetal weight estimation or inappropriate choice of delivery models. The early screening and evaluation of macrosomia in the third trimester can improve delivery outcomes and reduce complications. However, traditional clinical and ultrasound examinations face difficulties in obtaining accurate fetal measurements during the third trimester of pregnancy. This study aims to develop a comprehensive predictive model for detecting macrosomia using machine learning (ML) algorithms. The accuracy of macrosomia prediction using logistic regression, k-nearest neighbors, support vector machine, random forest (RF), XGBoost, and LightGBM algorithms was explored. Each approach was trained and validated using data from 3244 pregnant women at a hospital in southern China. The information gain method was employed to identify deterministic features associated with the occurrence of macrosomia. The performance of six ML algorithms based on the recall and area under the curve evaluation metrics were compared. To develop an efficient prediction model, two sets of experiments based on ultrasound examination records within 1-7 days and 8-14 days prior to delivery were conducted. The ensemble model, comprising the RF, XGBoost, and LightGBM algorithms, showed encouraging results. For each experimental group, the proposed ensemble model outperformed other ML approaches and the traditional Hadlock formula. The experimental results indicate that, with the most risk-relevant features, the ML algorithms presented in this study can predict macrosomia and assist obstetricians in selecting more appropriate delivery models.
由于胎儿体重估计错误或分娩方式选择不当，巨大胎儿与孕产妇及新生儿并发症相关。孕晚期对巨大胎儿进行早期筛查和评估可改善分娩结局并减少并发症。然而，传统的临床和超声检查在孕晚期获取准确的胎儿测量数据时面临困难。本研究旨在使用机器学习（ML）算法开发一种用于检测巨大胎儿的综合预测模型。探索了使用逻辑回归、k近邻、支持向量机、随机森林（RF）、XGBoost和LightGBM算法预测巨大胎儿的准确性。每种方法都使用中国南方一家医院3244名孕妇的数据进行训练和验证。采用信息增益方法识别与巨大胎儿发生相关的决定性特征。比较了基于召回率和曲线下面积评估指标的六种ML算法的性能。为了开发一种高效的预测模型，基于分娩前1 - 7天和8 - 14天内的超声检查记录进行了两组实验。由RF、XGBoost和LightGBM算法组成的集成模型显示出令人鼓舞的结果。对于每个实验组，所提出的集成模型优于其他ML方法和传统的哈德洛克公式。实验结果表明，利用最相关的风险特征，本研究中提出的ML算法可以预测巨大胎儿，并协助产科医生选择更合适的分娩方式。
