Machine Learning Approaches to predict Intra-Uterine Insemination Success Rate- Application of Artificial Intelligence in Infertility
Men's Health Journal,
Vol. 5 No. 1 (2021),
6 January 2021
,
Page e9
https://doi.org/10.22037/mhj.v5i1.34250
Abstract
Introduction: Assisted Reproductive Technology (ART) has been widely utilized for infertility management. Despite its low success rate, Intra-Uterine Insemination (IUI) is one of the first alternatives and most important approaches regarding many cases of infertility treatment. Given the numerous influencing factors and limitations associated with time and resources, the development of a reliable model to predict the success rate of ART methods can significantly contribute to decision-making processes. Materials and methods: We reviewed the demographic, clinical, and laboratory data regarding 157 IUI treatment cycles among 124 women using their partner’s sperm from May2017 to June2019. Primary outcome measures were clinical pregnancy and live birth. Some prediction models were constructed and compared to the logistic regression analysis. Results: Woman’s mean age was 30.1 ± 5.2 years and the infertility had a female cause in 24.3% of the cases, male cause in 32.6% of cases, and combined causes in 32.6% of the cases. Concerning the first IUI cycle, the clinical pregnancy rate per cycle was 16.9% (N= 21). Data were prepared according to cross-industry standard process for data mining (CRISP-DM) methodology, and the following models were fitted to the data: J48 Decision Tree, Perceptron Multilayer (MLP) Neural Network, Support Vector Machine (SVM) with radial basis function (RBF) kernel, K-Nearest Neighbors (KNN) with one neighborhood, and Bayesian Network. J48 Decision Tree, with a sensitivity of 95% and specificity of 98%, had the most optimal performance, and the KNN model was the weakest one. Conclusion: To predict the results of IUI as a simple and less invasive therapy for infertile couples, some models were applied based on artificial intelligence and J48 Decision Tree was recommended.
Keywords:
- Artificial intelligence
- Assisted Reproductive Technology (ART)
- Decision tree
- Infertility
- Insemination
- neural network
How to Cite
Allameh, F., Fallah-Karkan, M., Zadeh Modarres, S., Abedi, A. R., Eslami, M. J., Hajian, M. R., Dadpour, M., & Zareian, L. (2021). Machine Learning Approaches to predict Intra-Uterine Insemination Success Rate- Application of Artificial Intelligence in Infertility. Men’s Health Journal, 5(1), e9. https://doi.org/10.22037/mhj.v5i1.34250
References
1. Inhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Human reproduction update. 2015;21(4):411-26.
2. Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA. National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Health Surveys. PLoS Medicine. 2012;9(12):e1001356.
3. Ombelet W, Cooke I, Dyer S, Serour G, Devroey P. Infertility and the provision of infertility medical services in developing countries. Human reproduction update. 2008;14(6):605-21.
4. Vander Borght M, Wyns C. Fertility and infertility: Definition and epidemiology. Clinical Biochemistry. 2018;62:2-10.
5. Akhondi MM, Kamali K, Ranjbar F, Shirzad M, Shafeghati S, Ardakani ZB, et al. Prevalence of primary infertility in Iran in 2010. Iranian journal of public health. 2013;42(12):1398.
6. Kovalevsky G, Patrizio P. High rates of embryo wastage with use of assisted reproductive technology: a look at the trends between 1995 and 2001 in the United States. Fertility and sterility. 2005;84(2):325-30.
7. Shevell T, Malone FD, Vidaver J, Porter TF, Luthy DA, Comstock CH, et al. Assisted reproductive technology and pregnancy outcome. Obstetrics & Gynecology. 2005;106(5):1039-45.
8. Shibahara H, Obara H, Hirano Y, Suzuki T, Ohno A, Takamizawa S, et al. Prediction of pregnancy by intrauterine insemination using CASA estimates and strict criteria in patients with male factor infertility. International journal of andrology. 2004;27(2):63-8.
9. van Rumste MME, Custers IM, van Wely M, Koks CA, van Weering HGI, Beckers NGM, et al. IVF with planned single-embryo transfer versus IUI with ovarian stimulation in couples with unexplained subfertility: an economic analysis. Reproductive BioMedicine Online. 2014;28(3):336-42.
10. van Wely M. Intrauterine insemination. Infertility in Women with Polycystic Ovary Syndrome: Springer; 2018. p. 249-57.
11. McClamrock HD, Jones Jr HW, Adashi EY. Ovarian stimulation and intrauterine insemination at the quarter centennial: implications for the multiple births epidemic. Fertility and sterility. 2012;97(4):802-9.
12. Aboulghar M, Mansour R, Serour G, Abdrazek A, Amin Y, Rhodes C. Controlled ovarian hyperstimulation and intrauterine insemination for treatment of unexplained infertility should be limited to a maximum of three trials. Fertility and Sterility. 2001;75(1):88-91.
13. Intrauterine insemination. Hum Reprod Update. 2009;15(3):265-77.
14. Zadehmodarres S, Oladi B, Saeedi S, Jahed F, Ashraf H. Intrauterine insemination with husband semen: an evaluation of pregnancy rate and factors affecting outcome. Journal of assisted reproduction and genetics. 2009;26(1):7-11.
15. Ashrafi M, Rashidi M, Ghasemi A, Arabipour A, Daghighi S, Pourasghari P, et al. The Role of Infertility Etiology in Success Rate of Intrauterine Insemination Cycles: An Evaluation of Predictive Factors for Pregnancy Rate. International Journal of Fertility and Sterility. 2013;7(2):100-7.
16. Yousefi B, Azargon A. Predictive factors of intrauterine insemination success of women with infertility over 10 years. Journal of the Pakistan Medical Association. 2011:165-8.
17. Yumusak OH, Kahyaoglu S, Pekcan MK, Isci E, Cinar M, Tasci Y. Does intrauterine insemination timing matter for achieving pregnancy during ovulation induction using gonadotropins? A retrospective cohort study. Journal of the Chinese Medical Association. 2017;80(6):366-70.
18. Babak J, Behruz F, Mohammadreza Y, Morteza F-K. The Effect of Human Chorionic Gonadotropin Therapy on Semen Parameters and Pregnancy Rate after Varicocelectomy. Current urology. 2017;11(2):92-6.
19. Merviel P, Heraud MH, Grenier N, Lourdel E, Sanguinet P, Copin H. Predictive factors for pregnancy after intrauterine insemination (IUI): an analysis of 1038 cycles and a review of the literature. Fertility and sterility. 2010;93(1):79-88.
20. Styer AK, Jin S, Liu D, Wang B, Polotsky AJ, Christianson MS, et al. Association of uterine fibroids and pregnancy outcomes after ovarian stimulation-intrauterine insemination for unexplained infertility. Fertility and sterility. 2017;107(3):756-62.e3.
21. Lin J, Sun X-X. Predictive modeling in reproductive medicine. Reproductive and Developmental Medicine. 2018;2(4):224.
22. Curchoe CL, Malmsten J, Bormann C, Shafiee H, Flores-Saiffe Farias A, Mendizabal G, et al. Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us? Fertility and sterility. 2020;114(5):934-40.
23. Curchoe CL, Flores-Saiffe Farias A, Mendizabal-Ruiz G, Chavez-Badiola A. Evaluating predictive models in reproductive medicine. Fertility and sterility. 2020;114(5):921-6.
24. Vogiatzi P, Pouliakis A, Siristatidis C. An artificial neural network for the prediction of assisted reproduction outcome. Journal of assisted reproduction and genetics. 2019.
25. Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019;1(aop).
26. Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang G-Z. Big data for health. IEEE journal of biomedical and health informatics. 2015;19(4):1193-208.
27. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
28. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine. 2016;375(13):1216.
29. Allameh F, Qashqai H, Salavati A. A Dynamic Model for Predicting Prostate Cancer in Iranian Men Based on a Perceptron Neural Network. Int J Cancer Manag. 2017;10(3):e7415.
30. Bouton CE, Shaikhouni A, Annetta NV, Bockbrader MA, Friedenberg DA, Nielson DM, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature. 2016;533(7602):247.
31. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115.
32. Farina D, Vujaklija I, Sartori M, Kapelner T, Negro F, Jiang N, et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nature Biomedical Engineering. 2017;1(2):0025.
33. Jing Y, Bian Y, Hu Z, Wang L, Xie X-QS. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. The AAPS journal. 2018;20(3):58.
34. Johnson KW, Soto JT, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. Journal of the American College of Cardiology. 2018;71(23):2668-79.
35. Sniecinski I, Seghatchian J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfusion and Apheresis Science. 2018;57(3):422-4.
36. Somashekhar S, Kumar R, Kumar A, Patil P, Rauthan A. 551PD Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: An Indian experience. Annals of Oncology. 2016;27(suppl_9).
37. Manna C, Nanni L, Lumini A, Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reproductive biomedicine online. 2013;26(1):42-9.
38. Siristatidis C, Pouliakis A, Chrelias C, Kassanos D. Artificial intelligence in IVF: a need. Systems biology in reproductive medicine. 2011;57(4):179-85.
39. Siristatidis C, Vogiatzi P, Pouliakis A, Trivella M, Papantoniou N, Bettocchi S. Predicting IVF outcome: a proposed web-based system using artificial intelligence. In vivo (Athens, Greece). 2016;30(4):507-12.
40. Lamb D, Niederberger C. Artificial intelligence in medicine and male infertility. World journal of urology. 1993;11(2):129-36.
41. Ejzenberg D, Gomes TJ, Monteleone PA, Serafini PC, Soares Jr JM, Baracat EC. Prognostic factors for pregnancy after intrauterine insemination. International Journal of Gynecology & Obstetrics. 2019.
42. van Weert JM, Repping S, van der Steeg JW, Steures P, van der Veen F, Mol BW. IUI in male subfertility: are we able to select the proper patients? Reproductive biomedicine online. 2005;11(5):624-31.
43. Michau A, El Hachem H, Galey J, Le Parco S, Perdigao S, Guthauser B, et al. Predictive factors for pregnancy after controlled ovarian stimulation and intrauterine insemination: A retrospective analysis of 4146 cycles. Journal of gynecology obstetrics and human reproduction. 2019;48(10):811-5.
44. Atasever M, Kalem MN, Hatırnaz Ş, Hatırnaz E, Kalem Z, Kalaylıoğlu Z. Factors affecting clinical pregnancy rates after IUI for the treatment of unexplained infertility and mild male subfertility. Journal of the Turkish German Gynecological Association. 2016;17(3):134.
45. Blasco V, Prados N, Carranza F, González-Ravina C, Pellicer A, Fernández-Sánchez M. Influence of follicle rupture and uterine contractions on intrauterine insemination outcome: a new predictive model. Fertility and sterility. 2014;102(4):1034-40.
46. Lemmens L, Kos S, Beijer C, Brinkman JW, van der Horst FA, van den Hoven L, et al. Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination. Fertility and sterility. 2016;105(6):1462-8.
47. Yavuz A, Demirci O, Sözen H, Uludoğan M. Predictive factors influencing pregnancy rates after intrauterine insemination. Iranian journal of reproductive medicine. 2013;11(3):227.
48. Swierkowski-Blanchard N, Boitrelle F, Alter L, Selva J, Quibel T, Torre A. Uterine contractility and elastography as prognostic factors for pregnancy after intrauterine insemination. Fertility and sterility. 2017;107(4):961-8. e3.
49. Leushuis E, van der Steeg JW, Steures P, Bossuyt PM, Eijkemans MJ, van der Veen F, et al. Prediction models in reproductive medicine: a critical appraisal. Human reproduction update. 2009;15(5):537-52.
50. Steures P, Van Der Steeg JW, Mol BW, Eijkemans MJ, Van Der Veen F, Habbema JDF, et al. Prediction of an ongoing pregnancy after intrauterine insemination. Fertility and sterility. 2004;82(1):45-51.
51. Durairaj M, Thamilselvan P. Applications of artificial neural network for IVF data analysis and prediction. Journal of Engineering, Computers, and Applied Sciences. 2013;2(9):11-5.
52. Milewski R, Jankowska D, Cwalina U, Milewska AJ, Citko D, Więsak T, et al. Application of artificial neural networks and principal component analysis to predict results of infertility treatment using the IVF method. Studies in Logic, Grammar and Rhetoric. 2016;47(1):33-46.
53. Sahoo AJ, Kumar Y. Seminal quality prediction using data mining methods. Technology and health care : official journal of the European Society for Engineering and Medicine. 2014;22(4):531-45.
54. Yi WJ, Park KS, Paick JS. Morphological classification of sperm heads using artificial neural networks. Studies in health technology and informatics. 1998;52 Pt 2:1071-4.
55. Milewska AJ, Jankowska D, Cwalina U, Więsak T, Citko D, Morgan A, et al. Analyzing Outcomes of Intrauterine Insemination Treatment by Application of Cluster Analysis or Kohonen Neural Networks. Studies in Logic, Grammar and Rhetoric. 2013;35(1):7-25.
2. Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA. National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Health Surveys. PLoS Medicine. 2012;9(12):e1001356.
3. Ombelet W, Cooke I, Dyer S, Serour G, Devroey P. Infertility and the provision of infertility medical services in developing countries. Human reproduction update. 2008;14(6):605-21.
4. Vander Borght M, Wyns C. Fertility and infertility: Definition and epidemiology. Clinical Biochemistry. 2018;62:2-10.
5. Akhondi MM, Kamali K, Ranjbar F, Shirzad M, Shafeghati S, Ardakani ZB, et al. Prevalence of primary infertility in Iran in 2010. Iranian journal of public health. 2013;42(12):1398.
6. Kovalevsky G, Patrizio P. High rates of embryo wastage with use of assisted reproductive technology: a look at the trends between 1995 and 2001 in the United States. Fertility and sterility. 2005;84(2):325-30.
7. Shevell T, Malone FD, Vidaver J, Porter TF, Luthy DA, Comstock CH, et al. Assisted reproductive technology and pregnancy outcome. Obstetrics & Gynecology. 2005;106(5):1039-45.
8. Shibahara H, Obara H, Hirano Y, Suzuki T, Ohno A, Takamizawa S, et al. Prediction of pregnancy by intrauterine insemination using CASA estimates and strict criteria in patients with male factor infertility. International journal of andrology. 2004;27(2):63-8.
9. van Rumste MME, Custers IM, van Wely M, Koks CA, van Weering HGI, Beckers NGM, et al. IVF with planned single-embryo transfer versus IUI with ovarian stimulation in couples with unexplained subfertility: an economic analysis. Reproductive BioMedicine Online. 2014;28(3):336-42.
10. van Wely M. Intrauterine insemination. Infertility in Women with Polycystic Ovary Syndrome: Springer; 2018. p. 249-57.
11. McClamrock HD, Jones Jr HW, Adashi EY. Ovarian stimulation and intrauterine insemination at the quarter centennial: implications for the multiple births epidemic. Fertility and sterility. 2012;97(4):802-9.
12. Aboulghar M, Mansour R, Serour G, Abdrazek A, Amin Y, Rhodes C. Controlled ovarian hyperstimulation and intrauterine insemination for treatment of unexplained infertility should be limited to a maximum of three trials. Fertility and Sterility. 2001;75(1):88-91.
13. Intrauterine insemination. Hum Reprod Update. 2009;15(3):265-77.
14. Zadehmodarres S, Oladi B, Saeedi S, Jahed F, Ashraf H. Intrauterine insemination with husband semen: an evaluation of pregnancy rate and factors affecting outcome. Journal of assisted reproduction and genetics. 2009;26(1):7-11.
15. Ashrafi M, Rashidi M, Ghasemi A, Arabipour A, Daghighi S, Pourasghari P, et al. The Role of Infertility Etiology in Success Rate of Intrauterine Insemination Cycles: An Evaluation of Predictive Factors for Pregnancy Rate. International Journal of Fertility and Sterility. 2013;7(2):100-7.
16. Yousefi B, Azargon A. Predictive factors of intrauterine insemination success of women with infertility over 10 years. Journal of the Pakistan Medical Association. 2011:165-8.
17. Yumusak OH, Kahyaoglu S, Pekcan MK, Isci E, Cinar M, Tasci Y. Does intrauterine insemination timing matter for achieving pregnancy during ovulation induction using gonadotropins? A retrospective cohort study. Journal of the Chinese Medical Association. 2017;80(6):366-70.
18. Babak J, Behruz F, Mohammadreza Y, Morteza F-K. The Effect of Human Chorionic Gonadotropin Therapy on Semen Parameters and Pregnancy Rate after Varicocelectomy. Current urology. 2017;11(2):92-6.
19. Merviel P, Heraud MH, Grenier N, Lourdel E, Sanguinet P, Copin H. Predictive factors for pregnancy after intrauterine insemination (IUI): an analysis of 1038 cycles and a review of the literature. Fertility and sterility. 2010;93(1):79-88.
20. Styer AK, Jin S, Liu D, Wang B, Polotsky AJ, Christianson MS, et al. Association of uterine fibroids and pregnancy outcomes after ovarian stimulation-intrauterine insemination for unexplained infertility. Fertility and sterility. 2017;107(3):756-62.e3.
21. Lin J, Sun X-X. Predictive modeling in reproductive medicine. Reproductive and Developmental Medicine. 2018;2(4):224.
22. Curchoe CL, Malmsten J, Bormann C, Shafiee H, Flores-Saiffe Farias A, Mendizabal G, et al. Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us? Fertility and sterility. 2020;114(5):934-40.
23. Curchoe CL, Flores-Saiffe Farias A, Mendizabal-Ruiz G, Chavez-Badiola A. Evaluating predictive models in reproductive medicine. Fertility and sterility. 2020;114(5):921-6.
24. Vogiatzi P, Pouliakis A, Siristatidis C. An artificial neural network for the prediction of assisted reproduction outcome. Journal of assisted reproduction and genetics. 2019.
25. Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019;1(aop).
26. Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang G-Z. Big data for health. IEEE journal of biomedical and health informatics. 2015;19(4):1193-208.
27. Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30.
28. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine. 2016;375(13):1216.
29. Allameh F, Qashqai H, Salavati A. A Dynamic Model for Predicting Prostate Cancer in Iranian Men Based on a Perceptron Neural Network. Int J Cancer Manag. 2017;10(3):e7415.
30. Bouton CE, Shaikhouni A, Annetta NV, Bockbrader MA, Friedenberg DA, Nielson DM, et al. Restoring cortical control of functional movement in a human with quadriplegia. Nature. 2016;533(7602):247.
31. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115.
32. Farina D, Vujaklija I, Sartori M, Kapelner T, Negro F, Jiang N, et al. Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation. Nature Biomedical Engineering. 2017;1(2):0025.
33. Jing Y, Bian Y, Hu Z, Wang L, Xie X-QS. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. The AAPS journal. 2018;20(3):58.
34. Johnson KW, Soto JT, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. Journal of the American College of Cardiology. 2018;71(23):2668-79.
35. Sniecinski I, Seghatchian J. Artificial intelligence: A joint narrative on potential use in pediatric stem and immune cell therapies and regenerative medicine. Transfusion and Apheresis Science. 2018;57(3):422-4.
36. Somashekhar S, Kumar R, Kumar A, Patil P, Rauthan A. 551PD Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: An Indian experience. Annals of Oncology. 2016;27(suppl_9).
37. Manna C, Nanni L, Lumini A, Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reproductive biomedicine online. 2013;26(1):42-9.
38. Siristatidis C, Pouliakis A, Chrelias C, Kassanos D. Artificial intelligence in IVF: a need. Systems biology in reproductive medicine. 2011;57(4):179-85.
39. Siristatidis C, Vogiatzi P, Pouliakis A, Trivella M, Papantoniou N, Bettocchi S. Predicting IVF outcome: a proposed web-based system using artificial intelligence. In vivo (Athens, Greece). 2016;30(4):507-12.
40. Lamb D, Niederberger C. Artificial intelligence in medicine and male infertility. World journal of urology. 1993;11(2):129-36.
41. Ejzenberg D, Gomes TJ, Monteleone PA, Serafini PC, Soares Jr JM, Baracat EC. Prognostic factors for pregnancy after intrauterine insemination. International Journal of Gynecology & Obstetrics. 2019.
42. van Weert JM, Repping S, van der Steeg JW, Steures P, van der Veen F, Mol BW. IUI in male subfertility: are we able to select the proper patients? Reproductive biomedicine online. 2005;11(5):624-31.
43. Michau A, El Hachem H, Galey J, Le Parco S, Perdigao S, Guthauser B, et al. Predictive factors for pregnancy after controlled ovarian stimulation and intrauterine insemination: A retrospective analysis of 4146 cycles. Journal of gynecology obstetrics and human reproduction. 2019;48(10):811-5.
44. Atasever M, Kalem MN, Hatırnaz Ş, Hatırnaz E, Kalem Z, Kalaylıoğlu Z. Factors affecting clinical pregnancy rates after IUI for the treatment of unexplained infertility and mild male subfertility. Journal of the Turkish German Gynecological Association. 2016;17(3):134.
45. Blasco V, Prados N, Carranza F, González-Ravina C, Pellicer A, Fernández-Sánchez M. Influence of follicle rupture and uterine contractions on intrauterine insemination outcome: a new predictive model. Fertility and sterility. 2014;102(4):1034-40.
46. Lemmens L, Kos S, Beijer C, Brinkman JW, van der Horst FA, van den Hoven L, et al. Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination. Fertility and sterility. 2016;105(6):1462-8.
47. Yavuz A, Demirci O, Sözen H, Uludoğan M. Predictive factors influencing pregnancy rates after intrauterine insemination. Iranian journal of reproductive medicine. 2013;11(3):227.
48. Swierkowski-Blanchard N, Boitrelle F, Alter L, Selva J, Quibel T, Torre A. Uterine contractility and elastography as prognostic factors for pregnancy after intrauterine insemination. Fertility and sterility. 2017;107(4):961-8. e3.
49. Leushuis E, van der Steeg JW, Steures P, Bossuyt PM, Eijkemans MJ, van der Veen F, et al. Prediction models in reproductive medicine: a critical appraisal. Human reproduction update. 2009;15(5):537-52.
50. Steures P, Van Der Steeg JW, Mol BW, Eijkemans MJ, Van Der Veen F, Habbema JDF, et al. Prediction of an ongoing pregnancy after intrauterine insemination. Fertility and sterility. 2004;82(1):45-51.
51. Durairaj M, Thamilselvan P. Applications of artificial neural network for IVF data analysis and prediction. Journal of Engineering, Computers, and Applied Sciences. 2013;2(9):11-5.
52. Milewski R, Jankowska D, Cwalina U, Milewska AJ, Citko D, Więsak T, et al. Application of artificial neural networks and principal component analysis to predict results of infertility treatment using the IVF method. Studies in Logic, Grammar and Rhetoric. 2016;47(1):33-46.
53. Sahoo AJ, Kumar Y. Seminal quality prediction using data mining methods. Technology and health care : official journal of the European Society for Engineering and Medicine. 2014;22(4):531-45.
54. Yi WJ, Park KS, Paick JS. Morphological classification of sperm heads using artificial neural networks. Studies in health technology and informatics. 1998;52 Pt 2:1071-4.
55. Milewska AJ, Jankowska D, Cwalina U, Więsak T, Citko D, Morgan A, et al. Analyzing Outcomes of Intrauterine Insemination Treatment by Application of Cluster Analysis or Kohonen Neural Networks. Studies in Logic, Grammar and Rhetoric. 2013;35(1):7-25.
- Abstract Viewed: 268 times
- pdf Downloaded: 231 times