Predicting the risk of threatened abortion using machine learning methods: a comparative study.

Journal: BMC pregnancy and childbirth
Published Date: Aug 30, 2025

Abstract

BACKGROUND AND OBJECTIVE: Threatened abortion, a common pregnancy complication that often leading to abortion, is hard to predict due to its non-specific symptoms and difficulty in differentiating from other early pregnancy bleeding causes. Current diagnostic methods like serial ultrasounds and clinical monitoring are time-consuming and lack timeliness. To fill the gap in using advanced analytics for early detection and risk stratification, this study develops a machine learning (ML) model based on routine blood data to better predict threatened abortion, providing a reference for early detection and intervention.

Authors

  • Zhenning Zhu
    The Second Affiliated Hospital of Shaanxi University of Chinese Medicine, Gynecology Department, Xianyang, 712000, China.
  • Na Wei
    Department of Obstetrics and Gynecology, Weifang Yi Du Center Hospital, Qingzhou, Shandong, PR China.
  • Junjie Guo
    Department of Medical Imaging, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, No.1 Panfu Road, Yuexiu District, Guangzhou, 510030, Guangdong, China. gjunjie08@163.com.
  • Changlei Yue
    Beijing Goldwind Yi Tong Technology Co., LTD, Beijing, 100000, China.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Zicheng Zhang
    School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P. R. China.
  • ShiYu Wu
  • Jie Su
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Biao Song
    Inner Mongolia Wesure Date Technology Co., Ltd, Inner Mongolia, P.R. China.

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