Prediction of spontaneous preterm birth in pregnant women using machine learning.

Journal: Archives of gynecology and obstetrics
Published Date: Jul 12, 2025

Abstract

PURPOSE: Spontaneous preterm birth (sPTB) is a significant global health concern, contributing to adverse outcomes for both pregnant women and newborns. Early identification of women with risk of sPTB is essential for mitigating these negative effects and improving maternal and neonatal health outcomes. The aim of this study is to explore the feasibility of using machine learning to predict sPTB risk and to analyze the contribution of variables.

Authors

  • Xiaoxue Yang
    Ultrasonic Diagnosis Center, Northwest Women's and Children's Hospital, No. 1616, Yanxiang Rd, Xi'an, 710061, Shaanxi, China.
  • Xuewu Song
    Department of Pharmacy, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Kun Yang
    Department of Bone and Joint Surgery, Affiliated Hospital of Southwest Medical University, Luzhou Sichuan, 646000, P.R.China.
  • Peng Gao
    Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, United States.
  • Shuai Wang
    Department of Intensive Care Unit, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Simin Zhang
    Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
  • Rong Qiang
    Medical Genetics Center, Northwest Women's and Children's Hospital, Xi'an, Shaanxi, China.
  • Zhibin Li
    Epidemiology Research Unit, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com.
  • Xinru Gao
    Marine Biomedical Research Institute of Qingdao, School of Medicine and Pharmacy, Key Laboratory of Marine Drugs, Chinese Ministry of Education, Ocean University of China, Qingdao, P. R. China.

Keywords

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