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Machine learning vs. classic statistics for the prediction of IVF outcomes

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

Abstract

Purpose

To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.

Methods

The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data.

Results

Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models.

Conclusions

Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists’ counselling and their patients in adjusting the appropriate treatment strategy.

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Acknowledgments

We would like to thank Dr. Tamar Berman for her insightful comments.

Availability of data and material

Data will be shared upon request.

Funding

This work was supported by the National Institutes of Health (grant numbers P30ES00002, R21ES024236, and K99ES026648) and by the Environment and Health Fund, Israel (grant award no. RPGA1301).

Author information

Authors and Affiliations

  1. Public Health Services, Ministry of Health, 39 Yirmiyahu Street, 9446724, Jerusalem, Israel

    Zohar Barnett-Itzhaki

  2. School of Engineering, Ruppin Academic Center, Emek Hefer, Israel

    Zohar Barnett-Itzhaki

  3. Research Center for Health Informatics, Ruppin Academic Center, Emek Hefer, Israel

    Zohar Barnett-Itzhaki

  4. Bioinformatics Department, School of Life and Health Sciences, Jerusalem College of Technology, Jerusalem, Israel

    Zohar Barnett-Itzhaki, Miriam Elbaz, Rachely Butterman, Devora Amar & Moshe Amitay

  5. Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA

    Catherine Racowsky

  6. Department of Obstetrics and Gynecology, Sheba Medical Center, 52561, Ramat Gan, Israel

    Raoul Orvieto & Ronit Machtinger

  7. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    Raoul Orvieto & Ronit Machtinger

  8. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA

    Russ Hauser

  9. Laboratory of Precision Environmental Biosciences, Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, NY, 10032, USA

    Andrea A. Baccarelli

Authors
  1. Zohar Barnett-Itzhaki
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  2. Miriam Elbaz
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  3. Rachely Butterman
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  4. Devora Amar
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  5. Moshe Amitay
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  6. Catherine Racowsky
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  7. Raoul Orvieto
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  8. Russ Hauser
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  9. Andrea A. Baccarelli
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  10. Ronit Machtinger
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Corresponding author

Correspondence to Zohar Barnett-Itzhaki.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Sheba (8707/11).

Consent to participate

All patients signed an informed consent according to the IRB approval.

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Yes

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Barnett-Itzhaki, Z., Elbaz, M., Butterman, R. et al. Machine learning vs. classic statistics for the prediction of IVF outcomes. J Assist Reprod Genet 37, 2405–2412 (2020). https://doi.org/10.1007/s10815-020-01908-1

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  • DOI: https://doi.org/10.1007/s10815-020-01908-1

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