IVF Success Prediction using Machine Learning Techniques: A Comparative Study
Keywords:
IVF, Machine Learning, Random Forest, Prediction, Assisted Reproductive TechnologyAbstract
IVF is a popularly used assisted reproductive therapy that aids couples who are having trouble getting pregnant naturally. Medical providers must be able to predict an IVF cycle's effectiveness to tailor their care and enhance outcomes. To forecast the likelihood of a successful IVF cycle, this study proposes a machine learning (ML) model based on the random forest approach.
A dataset of patient cycles was collected from the author of a research paper we came across while studying the topic. The dataset includes patient demographic and clinical variables, such as age, body mass index (BMI), semen test results, number of retrieved oocytes, and female and male infertility factors. After preprocessing and feature engineering, we used the random forest, support vector machine, gradient boosting, and logistic regression algorithms to build four classification models to predict IVF success and compared their results. The Gradient Boosting algorithm showed the highest accuracy of 87%, whereas the SVM model showed the least accuracy with 67%. The most important features for the prediction were age, number of retrieved oocytes, and embryo quality, consistent with previous studies. The research shows the potential of machine learning models for predicting IVF success, which can assist physicians in making wise choices and enhancing the results for patients. To confirm the generalizability of the approach, additional studies with larger datasets and more varied patient populations are required.
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Siristatidis, C., Pouliakis, A., Chrelias, C., & Kassanos, D. (2011). Artificial Intelligence in IVF: A Need. Systems Biology in Reproductive Medicine, 57(4), 179–185. https://doi.org/10.3109/19396368.2011.558607
Lintsen, A. M. E., Eijkemans, M. J. C., Hunault, C. C., Bouwmans, C. A. M., Hakkaart, L., Habbema, J. D. F., & Braat, D. D. M. (2007). Predicting ongoing pregnancy chances after IVF and ICSI: a national prospective study. Human Reproduction, 22(9), 2455–2462. https://doi.org/10.1093/humrep/dem183
Durairaj, M. & Nandha Kumar, R. (2013). Data mining application on IVF data for the selection of influential parameters on fertility. International Journal of Engineering and Advanced Technology (IJEAT), 2(6), 262-266. Retrieval Number: F2068082613/2013©BEIESP. https://www.ijeat.org/portfolio-item/f2068082613/
Durairaj, M., & Thamilselvan, P. (2013). Applications of Artificial Neural Network for IVF Data Analysis and Prediction. Journal of Engineering Computers & Applied Sciences. https://www.semanticscholar.org/paper/Applications-of-Artificial-Neural-Network-for-IVF-Durairaj-Thamilselvan/9232f53eb29a933be580eb050a9df8abda922981
Vogiatzi, P., Pouliakis, A., & Siristatidis, C. (2019). An artificial neural network for the prediction of assisted reproduction outcome. Journal of assisted reproduction and genetics, 36(7), 1441–1448. https://doi.org/10.1007/s10815-019-01498-7
Blank, C., Wildeboer, R. R., DeCroo, I., Tilleman, K., Weyers, B., de Sutter, P., Mischi, M., & Schoot, B. C. (2019). Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertility and Sterility, 111(2), 318–326. https://doi.org/10.1016/j.fertnstert.2018.10.030
Tadepalli, S. K., & Lakshmi, P. V. (2019). Application of Machine Learning and Artificial Intelligence Techniques for IVF Analysis and Prediction. International Journal of Big Data and Analytics in Healthcare, 4(2), 21–33. https://doi.org/10.4018/ijbdah.2019070102
Raef, B., Maleki, M., & Ferdousi, R. (2020). Computational prediction of implantation outcome after embryo transfer. Health informatics journal, 26(3), 1810–1826. https://doi.org/10.1177/1460458219892138
Handayani, N., Louis, C. M., Erwin, A., Aprilliana, T., Polim, A. A., Sirait, B. I., Boediono, A., & Sini, I. (2022). Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program. FERTILITY & REPRODUCTION, 4(2), 1–11. http://repository.uki.ac.id/8721/
Sujata, N., Patil, Uday Wali, Swamy, M., & Patil, N. (2018). Deep Learning Techniques for Automatic Classification and Analysis of Human in Vitro Fertilized (IVF) embryos. JETIR1802014 Journal of Emerging Technologies and Innovative Research, 5. https://www.jetir.org/papers/JETIR1802014.pdf
C. C. Aggarwal and C. K. Reddy, Data clustering: algorithms and applications. CRC Press, 2013.
P. N. Tan, K. Steinbach, and V. Kumar, “Data mining cluster analysis: Basic concepts and algorithms,” 2006.
T. M. Mitchell, “Machine learning. 1997,” Burr Ridge, IL: McGraw Hill, vol. 45, 1997
K. Meena, M. Durairaj, and K. R. Subramanian, “Machine Learning Techniques of Artificial Neural Network Modeling to Predict Fertility Rate of Sperm from the Outcome of IVF Functional Tests”, International Journal of Computer Science and Applications.
M. Durairaj, K. Meena, and S. Selvaraju, ―Applying a data mining approach of rough sets on spermatological data analysis as predictors of in-vitro fertility of bull semen‖, International Journal of Computer Mathematical Sciences and Applications, Serials Publications, ISSN: 0973-6786, Vol. 2(3), pp. 221-231, Dec 2008.
Kaufmann, S.J., Eastaugh, J.L., Snowden, S., Smye, S.W. and Sharma, V. The Application of neural networks in predicting the outcome of in-vitro fertilization. Human Reproduction, (1997) vol.12 no. 7 pp. 1544-1457.
Larsson, B. and Rodriguez-Martinez, H. Can we use in vitro fertilization tests to predictsemen fertility? Anim. Reprod. Sci. (2000) 60-61: 327-336.
S.J.Kaufmann, J.L.Eastaugh, S.Snowden, S.W.Smye and V.Sharma (1997)‖The application of Neural Networks in predicting the outcome of in-vitro fertilization ―Human Reproduction vol.12 no. 7 pp.1454–1457.
Kay Elder, & Brian Dale., 2000, ―In- Vitro Fertilization‖, Second Edition, United Kingdom at the University Press, Cambridge.
Barnett-Itzhaki Z, Elbaz M, Butterman R, et al. Machine learning vs. classic statistics for the prediction of IVF outcomes. J Assist Reprod Genet. 2020;37(10):2405–12. https://doi.org/10.1007/s10815-020- 01908-1
Passmore L, Goodside J, Hamel L, Gonzales L, Silberstein T, Trimarchi J. Assessing decision tree models for clinical in-vitro fertilization data. December 2003:1–15. http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.131.7597&rep=rep1& type=pdf
https://www.cdc.gov/art/ivf-success-estimator/
https://www.arcfertility.com/sart-patient-predictor/
https://machinelearningmastery.com/basic-data-cleaning-for-machine-learning/
https://link.springer.com/article/10.1007/s00521-018-3693-9
Shixia Liu, Xiting Wang, Mengchen Liu, Jun Zhu, Towards better analysis of machine learning models: A visual analytics perspective, Visual Informatics, Volume 1, Issue 1, 2017, Pages 48-56, ISSN 2468-502X, https://doi.org/10.1016/j.visinf.2017.01.006.
Ms. Mohini Dadhe, Ms. Sneha Miskin. (2015). Optimized Wireless Stethoscope Using Butterworth Filter. International Journal of New Practices in Management and Engineering, 4(03), 01 - 05. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/37
Raj, R., & Sahoo, D. S. S. . (2021). Detection of Botnet Using Deep Learning Architecture Using Chrome 23 Pattern with IOT. Research Journal of Computer Systems and Engineering, 2(2), 38:44. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/31
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