Construction and evaluation of machine learning-based prediction model for live birth following fresh embryo transfer in IVF/ICSI patients with polycystic ovary syndrome.

Journal: Journal of ovarian research
PMID: 40186314

Abstract

OBJECTIVE: To investigate the determinants affecting live birth outcomes in fresh embryo transfer among polycystic ovary syndrome (PCOS) patients using various machine learning (ML) algorithms and to construct predictive models, offering novel insights for enhancing live birth rates in this specific group.

Authors

  • Suqin Zhu
    Center of Reproductive Medicine, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Maternity and Child Health Hospital, Fujian Medical University, No. 18 Daoshan Road, Fuzhou City, 350001, Fujian Province, China.
  • Zhiqing Huang
    Center of Reproductive Medicine, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Maternity and Child Health Hospital, Fujian Medical University, No. 18 Daoshan Road, Fuzhou City, 350001, Fujian Province, China.
  • Xiaojing Chen
    Department of Computer Science and Engineering, University of California, Riverside, CA, USA.
  • Wenwen Jiang
    Center of Reproductive Medicine, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Maternity and Child Health Hospital, Fujian Medical University, No. 18 Daoshan Road, Fuzhou City, 350001, Fujian Province, China.
  • Yuan Zhou
    Department of Pharmacy, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
  • Beihong Zheng
    Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou 350001, China.
  • Yan Sun
    Department of Biochemistry, Albert Einstein College of Medicine, New York, NY, United States.

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