Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China.

Journal: BMC pregnancy and childbirth
PMID: 39934718

Abstract

BACKGROUND: Macrosomia presents significant risks to both maternal and neonatal health, however, accurate antenatal prediction remains a major challenge. This study aimed to develop machine learning approaches to enhance the prediction of fetal macrosomia at different stages of pregnancy.

Authors

  • Qingyuan Liu
    College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.
  • Simin Zhu
    Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Meng Zhao
    School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
  • Lan Ma
    School of Math and Statistic, Suzhou University, Suzhou, Anhui 23400, China.
  • Chenqian Wang
    Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
  • Xiaotong Sun
    College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
  • Yanyan Feng
    Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
  • Yifan Wu
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Zhen Zeng
    Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.
  • Lei Zhang
    Division of Gastroenterology, Union Hospital, Tongji Medical College Medical College, Huazhong University of Science and Technology, Wuhan, China.

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