Machine learning approach for the prediction of macrosomia  

作　　者：Xiaochen Gu Ping Huang Xiaohua Xu Zhicheng Zheng Kaiju Luo Yujie Xu Yizhen Jia Yongjin Zhou 

机构地区：[1]Eye Hospital,Wenzhou Medical University,Wenzhou,Zheijang 325027,China [2]School of Biomedical Engineering,Medical School,Shenzhen University,Shenzen,Guangdong 518058,China [3]Marshall Laboratory of Biomedical Engineering,Shenzen,Guangdong 518058,China [4]Division of Ultrasound,Department of Medical Imaging,the University of Hong Kong-Shenzhen Hospital,Shenzen,Guangdong 518058,China [5]Ultrasound Department,the First Affiliated Hospital of Shenzhen University,Second People’s Hospital,Shenzen,Guangdong,China 518058 [6]Core Laboratory,the University of Hong Kong-Shenzhen Hospital,Shenzen,Guangdong 518058,China

出　　处：《Visual Computing for Industry,Biomedicine,and Art》2024年第1期132-141,共10页工医艺的可视计算(英文)

基　　金：supported by the High Level-Hospital Program,Health Commission of Guangdong Province,China,No.HKUSZH201901011;the Shenzhen Science and Technology Program,No.JCYJ20220530142017038.

摘　　要：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 tra-ditional Hadlock formula.The experimental results indicate that,with the most risk-relevant features,the ML algo-rithms presented in this study can predict macrosomia and assist obstetricians in selecting more appropriate delivery models.

关 键 词：MACROSOMIA Fetal weight prediction Machine learning algorithm Feature selection Ensemble learning 

分 类 号：TN9[电子电信—信息与通信工程]