Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population.

Journal: Journal of assisted reproduction and genetics
PMID: 38819714

Abstract

PURPOSE: This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population.

Authors

  • Jialin Wu
    Department of Computer Science, University of Texas at Austin, Austin, TX 78712, United States.
  • Tingting Li
    Key Laboratory of Biotechnology and Bioresources Utilization (Dalian Minzu University), Ministry of Education, Dalian, China.
  • Linan Xu
    Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China.
  • Lina Chen
    Department of Ophthalmology, The Third People's Hospital of Dalian, Dalian, Liaoning Province, China.
  • Xiaoyan Liang
    Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China.
  • Aihua Lin
    School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China.
  • Wangjian Zhang
    Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer 12144, USA.
  • Rui Huang
    Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.

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