Experience in machine learning application to predict pregnancy loss after assisted reproductive technologies
Drapkina Yu.S., Makarova N.P., Kalinin A.P., Vasiliev R.A., Amelin V.V.
Relevance: Machine learning (ML) method of data analysis makes it possible to thoroughly analyze the predictors of pregnancy loss after assisted reproductive technologies (ART). Prediction of live birth rate in ART program can be made using traditional mathematical models. However, ML enables to discover hidden patterns in nonlinear relationships and determine additional correctable factors.
Objective: Prediction of miscarriage in patients who undergo infertility treatment using ART methods based on clinical, anamnestic and embryological parameters, using the decision tree algorithm combined with linear regression.
Materials and methods: The retrospective study included 1021 married couples. The study analyzed the results of clinical and laboratory examination and the parameters of stimulated cycle depending on the rates of pregnancy and miscarriage after ART using linear regression and decision tree.
Results: The most important predictors of miscarriage in ART programs were detected using two models, including age, medical history of pregnancies from the particular partner, duration of stimulation, embryo quality, as well as fertilization method.
Conslusion: Research in this area, especially using ML tools for data processing makes it possible to build a software product for personalized and integrated prediction of live births for each married couple. The obtained results can optimize the state’s financial and economic expenditures to conduct ART cycles at the expense of Compulsory Health Insurance for different groups of patients. In addition, a clear and unified algorithm facilitates the targeted impact on the most probable cause of miscarriage, taking into account optimization of product preparation time and achievement of maximum effect to reduce the rate of pregnancy loss after ART.
Authors' contributions: Drapkina Yu.S. – article writing; Makarova N.P. – the concept and design of the study, text editing; Kalinin A.P. – collection of literature data; Vasiliev R.A., Amelin V.V. – material processing, mathematical model building.
Conflicts of interest: The authors confirm that they have no conflict of interest to declare.
Funding: The study was conducted without any sponsorship.
Ethical Approval: The study was approved by the local Ethics Committee of V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, the Ministry of Health of Russia.
Patient Consent for Publication: The patients have signed informed consent for publication of their data.
Authors' Data Sharing Statement: The data supporting the findings of this study are available on request from the corresponding author after approval from the principal investigator.
For citation: Drapkina Yu.S., Makarova N.P., Kalinin A.P., Vasiliev R.A., Amelin V.V. Experience
in machine learning application to predict pregnancy loss after assisted reproductive technologies.
Akusherstvo i Gynecologia/Obstetrics and Gynecology. 2024; (9): 90-98 (in Russian)
https://dx.doi.org/10.18565/aig.2024.157
Keywords
pregnancy
assisted reproductive technology
pregnancy loss/miscarriage
machine learning
delivery
References
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Received 06.07.2024
Accepted 23.09.2024
About the Authors
Yulia S. Drapkina, PhD, Senior Researcher, Department of IVF named after Prof. B.V. Leonov, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Academician Oparin str., 4, yu_drapkina@oparina4.ru,https://orcid.org/0000-0002-0545-1607
Natalya P. Makarova, PhD, Leading Researcher, Department of IVF named after Prof. B.V. Leonov, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Academician Oparin str., 4, np_makarova@oparina4.ru,
https://orcid.org/0000-0003-8922-2878
Andrey P. Kalinin, student of the Faculty of Medicine, N.I. Pirogov Russian National Research Medical University, Ministry of Health of Russia, 117997, Russia, Moscow, Ostrovityanov str., 1, zoaza8@mail.ru
Robert A. Vasiliev, Head of the Laboratory of Applied Artificial Intelligence Z-union; Vice-president of the Association of Laboratories for the Development of Artificial Intelligence; graduate student at the Moscow Institute of Physics and Technology; Master of the Department of Applied Physics and Mathematics of the Moscow Institute of Physics and Technology; Master of Economics; Bachelor’s degree at the Research University «Moscow Institute of Electronic Technology».
Vladislav V. Amelin, Technical Director of the Laboratory of Applied Artificial Intelligence Z-union, expert in machine learning; Master’s degree from Moscow State University (Faculty of Computational Mathematics and Cybernetics, Department of Mathematical Methods); Bachelor’s degree from the National Research University «Moscow Institute of Electronic Technology».
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