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A machine learning approach for prediction of pregnancy outcome following IVF treatment

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Abstract

Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature.

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References

  1. Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38(3):401–408

    Article  Google Scholar 

  2. Binder H, Schumacher M (2008) Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples. Stat Appl Genet Mol Biol 7(1):1–26 (Article 12)

    Article  MathSciNet  Google Scholar 

  3. Breiman L (1984) Classification and regression trees. Routledge, New York

    MATH  Google Scholar 

  4. Breiman L (1993) Classification and regression trees. CRC Press, Boca Raton

    MATH  Google Scholar 

  5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  6. Bustillo M, Stern JJ, King D, Coulam CB (1993) Serum progesterone and estradiol concentrations in the early diagnosis of ectopic pregnancy after in vitro fertilization-embryo transfer. Fertil Steril 59(3):668–670

    Article  Google Scholar 

  7. Charalambous C (1992) Conjugate gradient algorithm for efficient training of artificial neural networks. IEE Proc G-Circuits Dev Syst 13(3):301–310

    Article  Google Scholar 

  8. Choi B, Bosch E, Lannon BM, Leveille MC, Wong WH, Leader A, Pellicer A, Penzias AS, Yao MW (2013) Personalized prediction of first-cycle in vitro fertilization success. Fertil Steril 99(7):1905–1911

    Article  Google Scholar 

  9. Corani G, Magli C, Giusti A, Gianaroli L, Gambardella LM (2013) A Bayesian network model for predicting pregnancy after in vitro fertilization. Comput Biol Med 43(11):1783–1792

    Article  Google Scholar 

  10. Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88(11):2783–2792

    Article  Google Scholar 

  11. Demyttenaere K, Bonte L, Gheldof M, Vervaeke M, Meuleman C, Vanderschuerem D, D’Hooghe T (1998) Coping style and depression level influence outcome in in vitro fertilization. Fertil Steril 69(6):1026–1033

    Article  Google Scholar 

  12. Ding CH, Dubchak I (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17(4):349–358

    Article  Google Scholar 

  13. Durairaj M, Thamilselvan P (2013) Applications of artificial neural network for IVF data analysis and prediction. J Eng Comput Appl Sci 2(9):11–15

    Google Scholar 

  14. Durairaj M, Nandhakumar R (2014) An integrated methodology of artificial neural network and rough set theory for analyzing IVF data. In: 2014 International conference on intelligent computing applications (ICICA), pp 126–129

  15. Fayyad UM, Piatetsky-Shapiro G G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39(11):27–34

    Article  Google Scholar 

  16. Guh R, Wu TCJ, Weng SP (2011) Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes. Expert Syst Appl 38(4):4437–4449

    Article  Google Scholar 

  17. Güvenir HA, Misirli G, Dilbaz S, Ozdegirmenci O, Demir B, Dilbaz B (2015) Estimating the chance of success in IVF treatment using a ranking algorithm. Med Biol Eng Comput 53(9):911–920

    Article  Google Scholar 

  18. Hafiz P, Nematollahi M, Boostani R, Bahia NJ (2017) Predicting implantation outcome of In Vitro fertilization and intracytoplasmic sperm injection using data mining techniques. Fertil Steril 11(3):184–190

    Google Scholar 

  19. Haykin S (2004) Neural network—a comprehensive foundation. Prentice Hall, New Jersey

    MATH  Google Scholar 

  20. Hoover L, Baker A, Check JH, Lurie D, O’Shaughnessy A (1995) Evaluation of a new embryo-grading system to predict pregnancy rates following in vitro fertilization. Gynecol Obstet Invest 40(3):151–157

    Article  Google Scholar 

  21. Janecek A, Gansterer W, Demel M, Ecker G (2008) On the relationship between feature selection and classification accuracy. In: New challenges for feature selection in data mining and knowledge discovery, pp 90–105

  22. Jurisica I, Mylopoulos J, Glasgow J, Shapiro H, Casper RF (1998) Case-based reasoning in IVF: prediction and knowledge mining. Artif Intell Med 12(1):1–24

    Article  Google Scholar 

  23. Kaufmann SJ, Eastaugh JL, Snowden S, Smye SW, Sharma V (1997) The application of neural networks in predicting the outcome of in vitro fertilization. Hum Reprod 12(7):1454–1457

    Article  Google Scholar 

  24. Kaur H, Krishna D, Shetty N, Krishnan S, Srinivas MS, Rao KA (2012) Effect of pre-ovulatory single dose GnRH agonist therapy on IVF outcome in GnRH antagonist cycles; a prospective study. J Reprod Infertil 13(4):225–231

    Google Scholar 

  25. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: 14th International joint conference on artificial intelligence (IJCAI), vol 14(2), pp 1137–1143

  26. Manna C, Nanni L, Lumini A, Pappalardo S (2013) Artificial intelligence techniques for embryo and oocyte classification. Reprod Biomed Online 26(1):42–49

    Article  Google Scholar 

  27. Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA (2012) National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med 9(12):e1001356

    Article  Google Scholar 

  28. Milewski R, Milewska AJ, Wiesak T, Morgan A (2013) Comparison of artificial neural networks and logistic regression analysis in pregnancy prediction using the in vitro fertilization treatment. Stud Log Gramm Rhetor 35(1):39–48

    Article  Google Scholar 

  29. Morales DA, Bengoetxea E, Larrañaga P, García M, Franco Y, Fresnada M, Merino M (2008) Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Comput Methods Programs Biomed 90(2):104–116

    Article  Google Scholar 

  30. Nanni L, Lumini A, Manna C (2010) A data mining approach for predicting the pregnancy rate in human assisted reproduction. Adv Comput Intell Paradig Healthc 5:97–111

    Article  Google Scholar 

  31. Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines, Microsoft Research Technical Report MSR-TR-98-14

  32. Quinlan JR (1993) C4.5: programs for machine learning, vol 1. Morgan Kaufmann, San Francisco

    Google Scholar 

  33. Ramasamy N, Durairaj M (2017) Feature reduction by improvised hybrid algorithm for predicting the IVF success rate. J Adv Res Comput Sci 8(1):37–40

    Google Scholar 

  34. Schwarzer G, Vach W, Schumacher M (2000) On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 19(4):541–561

    Article  Google Scholar 

  35. SMO Weka (2017) Optimizing parameters. https://weka.wikispaces.com/Optimizing+parameters. Accessed 20 Dec 2017

  36. Uyar A, Bener A, Ciray HN, Bahceci M (2010) ROC based evaluation and comparison of classifiers for IVF implantation prediction. In: Electronic healthcare, pp 108–111

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

    Article  Google Scholar 

  38. Vapnik VN (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia

    Md Rafiul Hassan, Sadiq Al-Insaif & M. Imtiaz Hossain

  2. School of Engineering and Information Technology, Federation University, Ballarat, Australia

    Joarder Kamruzzaman

Authors
  1. Md Rafiul Hassan
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  2. Sadiq Al-Insaif
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  3. M. Imtiaz Hossain
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  4. Joarder Kamruzzaman
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Correspondence to Joarder Kamruzzaman.

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Hassan, M.R., Al-Insaif, S., Hossain, M.I. et al. A machine learning approach for prediction of pregnancy outcome following IVF treatment. Neural Comput & Applic 32, 2283–2297 (2020). https://doi.org/10.1007/s00521-018-3693-9

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  • DOI: https://doi.org/10.1007/s00521-018-3693-9

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