Skip to main content

Advertisement

Springer Nature Link
Log in

Predicting in vitro fertilization success in the Brazilian public health system: a machine learning approach

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Infertility has become a global health problem, increasing the number of couples looking for in vitro fertilization (IVF). Despite advances and technical improvements, some couples remain childless due to the high complexity of the technique. The use of machine learning (ML) in the prediction of pregnancy, computing factors that could interfere in the effectiveness of the treatment, is an important tool to optimize these factors and reach the success of pregnancy. The aim of this study was to apply ML models to determine variables related to pregnancy after IVF in a public health service, including pre-implantation variables. This study included 771 women who underwent IVF treatment at Hospital das Clínicas, Federal University of Minas Gerais, between 2013 and 2019. We used the following Machine Learning algorithms: Logistic Regression, Random Forest, XG Boost and Support Vector Machines. The Random Forest algorithm achieved the best performance, with better accuracy, sensitivity and area under the ROC curve to predict the success of IVF evaluated by pregnancy frequency. We also trained a specific model only for women older than 35 years old.

Graphical abstract

Variables in the Random Forest model related to pregnancy after in vitro fertilization

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Zegers-Hochschild F et al (2009) The International Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organization (WHO) revised glossary on ART terminology, 2009. Hum Reprod 24(11):2683–2687

    Article  CAS  Google Scholar 

  2. Kumar N, Singh AK (2015) Trends of male factor infertility, an important cause of infertility: a review of literature. Journal of Human Reproductive Sciences 8(4):191

    Article  Google Scholar 

  3. Kaufmann S, Eastaugh J, Snowden S, Smye S, Sharma V (1997) The application of neural networks in predicting the outcome of in-vitro fertilization. Human Reproduction (Oxford, England) 12(7):1454–1457

    Article  CAS  Google Scholar 

  4. Ramasamy, N. et al. Feature reduction by improvised hybrid algorithm for predicting the IVF success rate. International Journal of Advanced Research in Computer Science 8 (1) (2017)

  5. Hassan MR, Al-Insaif S, Hossain MI, Kamruzzaman J (2020) A machine learning approach for prediction of pregnancy outcome following IVF treatment. Neural Comput Appl 32(7):2283–2297

    Article  Google Scholar 

  6. Hafiz P, Nematollahi M, Boostani R, Jahromi BN (2017) Predicting implantation outcome of in vitro fertilization and intracytoplasmic sperm injection using data mining techniques. International journal of fertility & sterility 11(3):184

    Google Scholar 

  7. Blank C et al (2019) Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertil Steril 111(2):318–326

    Article  Google Scholar 

  8. Khosravi P et al (2019) Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digital Medicine 2(1):1–9

    Article  Google Scholar 

  9. Curchoe CL, Bormann CL (2019) Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet 36(4):591–600

    Article  Google Scholar 

  10. Inhorn MC, Patrizio P (2015) Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update 21(4):411–426

    Article  Google Scholar 

  11. Beam AL, Kohane IS (2018) Big data and machine learning in health care. JAMA 319(13):1317–1318

    Article  Google Scholar 

  12. Uyar A, Bener A, Ciray HN (2015) Predictive modeling of implantation outcome in an in vitro fertilization setting: an application of machine learning methods. Med Decis Making 35(6):714–725

    Article  Google Scholar 

  13. Crown WH (2015) Potential application of machine learning in health outcomes research and some statistical cautions. Value in Health 18(2):137–140

    Article  Google Scholar 

  14. Qiu J, Li P, Dong M, Xin X, Tan J (2019) Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method. J Transl Med 17(1):1–8

    Article  Google Scholar 

  15. Babitha, M. Survey on the machine learning techniques used in IVF treatment to improve the success rate (2019)

  16. Rodríguez-Pérez R, Bajorath J (2019) Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values. J Med Chem 63(16):8761–8777

    Article  Google Scholar 

  17. Zhao Y-Y, Yu Y, Zhang X-W (2018) Overall blastocyst quality, trophectoderm grade, and inner cell mass grade predict pregnancy outcome in euploid blastocyst transfer cycles. Chin Med J 131(11):1261–1267

    Article  Google Scholar 

  18. Lai I et al (2020) Transfers of lower quality embryos based on morphological appearance result in appreciable live birth rates: a Canadian center’s experience. F&S Reports 1(3):264–269

    Article  Google Scholar 

  19. Kühnlein M, Appelhans T, Thies B, Nauss T (2014) Improving the accuracy of rainfall rates from optical satellite sensors with machine learning-a random forests-based approach applied to MSG SEVIRI. Remote Sens Environ 141:129–143

    Article  Google Scholar 

  20. Brugo-Olmedo S, Chillik C, Kopelman S (2001) Definition and causes of infertility. Reprod Biomed Online 2(1):173–185

    Article  Google Scholar 

  21. Brandy E et al (2015) Births: final data for 2014. Nat Vital Stat Rep 64:1–64

    Google Scholar 

  22. De la Rochebrochard E, Thonneau P (2003) Paternal age \(\ge\) 40 years: an important risk factor for infertility. Am J Obstet Gynecol 189(4):901–905

    Article  Google Scholar 

  23. Rossi AC, Prefumo F (2016) The effects of surgery for endometriosis on pregnancy outcomes following in vitro fertilization and embryo transfer: a systematic review and meta-analysis. Arch Gynecol Obstet 294(3):647–655

    Article  Google Scholar 

  24. Murta M et al (2018) Endometriosis does not affect live birth rates of patients submitted to assisted reproduction techniques: analysis of the Latin American network registry database from 1995 to 2011. J Assist Reprod Genet 35(8):1395–1399

    Article  Google Scholar 

  25. Sharma S et al (2019) Does presence of adenomyosis affect reproductive outcome in IVF cycles? A retrospective analysis of 973 patients. Reprod Biomed Online 38(1):13–21

    Article  Google Scholar 

  26. Ballester M et al (2012) Cumulative pregnancy rate after ICSI-IVF in patients with colorectal endometriosis: results of a multicentre study. Hum Reprod 27(4):1043–1049

    Article  Google Scholar 

  27. Ferrero S et al (2015) Second surgery for recurrent unilateral endometriomas and impact on ovarian reserve: a case-control study. Fertil Steril 103(5):1236–1243

    Article  Google Scholar 

  28. Maignien C et al (2017) Prognostic factors for assisted reproductive technology in women with endometriosis-related infertility. Am J Obstet Gynecol 216(3):280-e1

    Article  Google Scholar 

  29. Vaegter KK et al (2017) Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertil Steril 107(3):641–648

    Article  Google Scholar 

  30. Choe SA et al (2011) Symmetry in number of retrieved oocytes between two ovaries: a possible predictor of in vitro fertilization outcome. Gynecol Endocrinol 27(12):997–1000

    Article  Google Scholar 

  31. Sunkara SK et al (2011) Association between the number of eggs and live birth in IVF treatment: an analysis of 400 135 treatment cycles. Hum Reprod 26(7):1768–1774

    Article  Google Scholar 

  32. Steward RG et al (2014) Oocyte number as a predictor for ovarian hyperstimulation syndrome and live birth: an analysis of 256,381 in vitro fertilization cycles. Fertil Steril 101(4):967–973

    Article  Google Scholar 

  33. Timeva T et al (2006) Correlation between number of retrieved oocytes and pregnancy rate after in vitro fertilization/intracytoplasmic sperm infection. Sci World J 6:686–690

    Article  Google Scholar 

  34. Bals-Pratsch M et al (2010) Extended analyses of the German IVF registry (DIR): andrological aspects, medical-economical assumptions related to the shift from IVF to ICSI and stimulation with gonadotropins. Journal für Reproduktionsmedizin und Endokrinologie-Journal of Reproductive Medicine and Endocrinology 7(1):40–44

    Google Scholar 

  35. Gambadauro P, Navaratnarajah R (2015) Reporting of embryo transfer methods in IVF research: a cross-sectional study. Reprod Biomed Online 30(2):137–143

    Article  Google Scholar 

  36. Brown, J., Buckingham, K., Buckett, W. & Abou-Setta, A. M. Ultrasound versus ’clinical touch’ for catheter guidance during embryo transfer in women. Cochrane database of systematic reviews (3) (2016)

  37. Ren X et al (2016) Effect of catheter choice during embryo transfer on the clinical outcome of in vitro fertilization-embryo transfer. Beijing da xue xue bao. Yi xue ban= Journal of Peking University. Health Sciences 48(5):905–909

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Análises Clínicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil

    Nayara C. N. Barreto, Ramon G. Pereira & Karina B. Gomes

  2. Programa de Pós-Graduação em Engenharia Elétrica, Faculdade de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Giulia Z. Castro

  3. Departamento de Ciências da Computação, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Ramon G. Pereira & Wagner M. Junior

  4. Laboratório de Reprodução Humana do Hospital da Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Francisco A. N. Pereira, Fernando M. Reis & Ines K. D. Cavallo

  5. Departamento de Ginecologia Obstetrícia, Faculdade de Medicina, Universidade Federal de Minas Gerais, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Fernando M. Reis

Authors
  1. Nayara C. N. Barreto
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Giulia Z. Castro
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ramon G. Pereira
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Francisco A. N. Pereira
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Fernando M. Reis
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Wagner M. Junior
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Ines K. D. Cavallo
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Karina B. Gomes
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conception: NCNB, FMR, KBG. Planning: NCNB, FANP, FMR, IKDC, KBG. Analyzing: NCNB, GZC, RGP, WMJ. Writing: NCNB, FMR, IKDC, KBG, GZC, RGP

Corresponding author

Correspondence to Karina B. Gomes.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

C. N. Barreto, N., Castro, G.Z., Pereira, R.G. et al. Predicting in vitro fertilization success in the Brazilian public health system: a machine learning approach. Med Biol Eng Comput 60, 1851–1861 (2022). https://doi.org/10.1007/s11517-022-02569-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-022-02569-1

Keywords