Machine Learning Based Assessment and Predictive Analysis of In-Vitro Fertilization Success Rate
DOI:
https://doi.org/10.4108/eetpht.10.5511Keywords:
In Vitro Fertilization, Machine Learning, Classification, Feature Selection, RegressionAbstract
INTRODUCTION: The transformation in the lifestyle and other societal and economic factors during modern times have led to rise in the cases of infertility among young generation. Apart from these factors infertility may also be attributed to different medical conditions among both men and women. This rise in the cases of infertility is a matter of huge concern to the mankind and should be seriously pondered upon. However, the unprecedented advancements in the field of healthcare have led to In Vitro fertilization as a rescue to this devastating condition. Although the In Vitro fertilization has the potential to unfurl the happiness, it has associated challenges also in terms of physical and emotional health. Also, the success rate of In Vitro fertilization may vary from person to person.
OBJECTIVES: To predict the success rate of In Vitro fertilization.
METHODS: Machine Learning Models.
RESULTS: It has been observed that Adaboost outperforms all other machine learning models by yielding an accuracy of 97.5%.
CONCLUSION: During the result analysis, it is concluded that if age > 36, there is a negative propensity for clinical pregnancy and if age >40, the probability of a clinical pregnancy dramatically declines. Further, the propensity of clinical pregnancy is positively correlated to the count of embryos transferred in the same IVF cycle.
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Foucaut, A.M., Faure, C., Julia, C., Czernichow, S., Levy, R., Dupont, C. and ALIFERT Collaborative Group, 2019. Sedentary behavior, physical inactivity and body composition in relation to idiopathic infertility among men and women. PloS one, 14(4), p.e0210770 DOI: https://doi.org/10.1371/journal.pone.0210770
https://www.who.int/news/item/04-04-2023-1-in-6-people-globally-affected-by-infertility
Blank, C., Wildeboer, R.R., DeCroo, I., Tilleman, K., Weyers, B., De Sutter, P., Mischi, M. and Schoot, B.C., 2019. Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertility and sterility, 111(2), pp.318-326. DOI: https://doi.org/10.1016/j.fertnstert.2018.10.030
Sujata, P.N., Madiwalar, S.M. and Aparanji, V.M., 2020, September. Machine Learning Techniques to Improve the Success Rate in In-Vitro Fertilization (IVF) Procedure. In IOP Conference Series: Materials Science and Engineering (Vol. 925, No. 1, p. 012039). IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/925/1/012039
Barnett-Itzhaki, Z., Elbaz, M., Butterman, R., Amar, D., Amitay, M., Racowsky, C., Orvieto, R., Hauser, R., Baccarelli, A.A. and Machtinger, R., 2020. Machine learning vs. classic statistics for the prediction of IVF outcomes. Journal of assisted reproduction and genetics, 37, pp.2405-2412. DOI: https://doi.org/10.1007/s10815-020-01908-1
Hassan, M.R., Al-Insaif, S., Hossain, M.I. and Kamruzzaman, J., 2020. A machine learning approach for prediction of pregnancy outcome following IVF treatment. Neural computing and applications, 32, pp.2283-2297. DOI: https://doi.org/10.1007/s00521-018-3693-9
Tadepalli, S.K. and 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 (IJBDAH), 4(2), pp.21-33. DOI: https://doi.org/10.4018/IJBDAH.2019070102
Liu, R., Bai, S., Jiang, X., Luo, L., Tong, X., Zheng, S., Wang, Y. and Xu, B., 2021. Multifactor prediction of embryo transfer outcomes based on a machine learning algorithm. Frontiers in Endocrinology, 12, p.745039. DOI: https://doi.org/10.3389/fendo.2021.745039
Sharma, N., Sharma, K.P., Mangla, M. and Rani, R., 2023. Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding. Multimedia Tools and Applications, 82(3), pp.4011-4029. DOI: https://doi.org/10.1007/s11042-022-13419-5
https://www.key2stats.com/data-set/view/1031
Mangla, M., Shinde, S.K., Mehta, V., Sharma, N. and Mohanty, S.N. eds., 2022. Handbook of Research on Machine Learning: Foundations and Applications. CRC Press. DOI: https://doi.org/10.1201/9781003277330
Sharma, N., Mangla, M., Yadav, S., Goyal, N., Singh, A., Verma, S. and Saber, T., 2021. A sequential ensemble model for photovoltaic power forecasting. Computers & Electrical Engineering, 96, p.107484. DOI: https://doi.org/10.1016/j.compeleceng.2021.107484
Mangla, M., Sharma, N. and Mohanty, S.N., 2021. A sequential ensemble model for software fault prediction. Innovations in Systems and Software Engineering, pp.1-8. DOI: https://doi.org/10.1007/s11334-021-00390-x
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Copyright (c) 2024 Vaishali Mehta, Monika Mangla, Nonita Sharma, Manik Rakhra, Tanupriya Choudhury, Garigipati Rama Krishna
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