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

  • Assisted Reproduction Technologies
  • Published:
Journal of Assisted Reproduction and Genetics Aims and scope Submit manuscript

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.

Methods

Results

A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914–0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy.

Conclusions

The machine learning–based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Author information

Author notes

  1. Wangjian Zhang and Rui Huang contributed equally to this work.

Authors and Affiliations

  1. Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China

    Jialin Wu, Tingting Li, Linan Xu, Lina Chen, **aoyan Liang & Rui Huang

  2. Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China

    Jialin Wu, Tingting Li, Linan Xu, Lina Chen, **aoyan Liang & Rui Huang

  3. Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China

    Jialin Wu, Tingting Li, Linan Xu, Lina Chen, **aoyan Liang & Rui Huang

  4. School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China

    Jialin Wu, Aihua Lin & Wangjian Zhang

Authors
  1. Jialin Wu
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Contributions

RH conceived and designed the research. WJZ provided statistical guidance. JLW analyzed the results and drafted the manuscript. TTL, LNC, and JLW performed the research and acquired the data. LNX, XYL, AHL, WJZ, RH, and JLW interpreted the data and modified the manuscript. All authors revised and approved the final version of the manuscript and agree to be accountable for all aspects of the work.

Corresponding authors

Correspondence to Wangjian Zhang or Rui Huang.

Ethics declarations

Ethics approval

This study has been approved by the Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-Sen University (2019ZSLYEC-011S).

Consent to participate

All patients provided written informed consent.

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The authors declare no competing interests.

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Wu, J., Li, T., Xu, L. et al. Development of a machine learning–based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population. J Assist Reprod Genet (2024). https://doi.org/10.1007/s10815-024-03153-2

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