Comparison of predictive models built with different machine learning techniques using the example of predicting the outcome of assisted reproductive technologies
- 作者: Drapkina Y.S.1, Makarova N.P.1, Vasiliev R.A.1, Amelin V.V.1, Kalinina E.A.1
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隶属关系:
- Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia
- 期: 编号 2 (2024)
- 页面: 97-105
- 栏目: Original Articles
- URL: https://journals.eco-vector.com/0300-9092/article/view/631360
- DOI: https://doi.org/10.18565/aig.2023.263
- ID: 631360
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详细
Advancements in machine learning (ML) have resulted in the development of numerous supporting software products for reproductive medicine. Predicting the performance of assisted reproductive technology (ART) using ML can be accomplished using different algorithms, depending on the type of data and specific task at hand.
Objective: This study aimed to compare the predictive ability of logistic regression, decision tree algorithm, and Random Forest in relation to the likelihood of pregnancy based on the clinical, anamnestic, and embryologic data of patients undergoing ART.
Materials and methods: This retrospective study included 854 married couples and analyzed clinical and laboratory data as well as parameters of the stimulated cycle in relation to the effectiveness of the ART program using three ML algorithms: logistic regression, decision tree, and Random Forest.
Results: The most accurate algorithm for predicting pregnancy rates in the ART program was the Random Forest model, which identified the significance of the following predictors: embryonic arrest, triggering of final oocyte maturation, number of embryos of excellent and average quality, duration of stimulation, infertility factor, body mass index, FSH, and AMH levels. The model confirmed the significance of the predictors determined in the previous stages of the study using a decision tree algorithm, including the presence/absence of a history of previous pregnancies, parameters of the stimulated cycle (number of MII oocytes), spermogram indicators on the day of the puncture, number of embryos of excellent and good quality, and quality of the embryo according to morphological evaluation criteria.
Conclusion: To enhance the prediction of ART effectiveness, this study suggests the need for better mathematical models with an integrated approach to solve the problem using a large sample of patients with various input data presented in a balanced volume. Additionally, this study suggests the inclusion of additional markers that determine ART effectiveness, thereby improving the accuracy of the software product.
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作者简介
Yulia Drapkina
Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia
编辑信件的主要联系方式.
Email: yu_drapkina@oparina4.ru
ORCID iD: 0000-0002-0545-1607
PhD, Researcher at the Department of IVF named after Prof. B.V. Leonov
俄罗斯联邦, 117997, Moscow, Ac. Oparina str., 4Natalya Makarova
Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia
Email: np_makarova@oparina4.ru
PhD, Leading Researcher at the Department of IVF named after Prof. B.V. Leonov
俄罗斯联邦, 117997, Moscow, Ac. Oparina str., 4Robert Vasiliev
Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia
Email: yu_drapkina@oparina4.ru
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 (MIPT), 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»
俄罗斯联邦, 117997, Moscow, Ac. Oparina str., 4Vladislav Amelin
Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia
Email: yu_drapkina@oparina4.ru
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»
俄罗斯联邦, 117997, Moscow, Ac. Oparina str., 4Elena Kalinina
Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia
Email: e_kalinina@oparina4.ru
ORCID iD: 0000-0002-8922-2878
Dr.Med. Sci., Professor, Head of the Department of IVF named after Prof. B.V. Leonov
俄罗斯联邦, 117997, Moscow, Ac. Oparina str., 4
参考
- Ившин А.А., Багаудин Т.З., Гусев А.В. Искусственный интеллект на страже репродуктивного здоровья. Акушерство и гинекология. 2021; 5: 17-24. [Ivshin A.A., Bagaudin T.Z., Gusev A.V. Artificial intelligence on guard of reproductive health. Obstetrics and Gynecology. 2021; (5): 17-24 (in Russian)]. https://dx.doi.org/10.18565/aig.2021.5.17-24.
- Драпкина Ю.С., Калинина Е.А., Макарова Н.П., Мильчаков К.С., Франкевич В.Е. Искусственный интеллект в репродуктивной медицине: этические и клинические аспекты. Акушерство и гинекология. 2022; 11: 37-44. [Drapkina Yu.S., Kalinina E.A., Makarova N.P., Milchakov K.S., Frankevich V.E. Artificial intelligence in reproductive medicine: ethical and clinical aspects. Obstetrics and Gynecology. 2022; (11): 37-44. (in Russian)]. https://dx.doi.org/10.18565/aig.2022.11.37-44.
- Акжолов Р.К. Машинное обучение. Вестник науки. 2019; 3(6): 348-51. [Akzholov R.K. Machine learning.Vestnik Nauki. 2019; 3(6): 348-51. (in Russian)].
- Хохлов А.Л., Белоусов Д.Ю. Этические аспекты применения программного обеспечения с технологией искусственного интеллекта. Качественная клиническая практика. 2021; 1: 70-84. [Khokhlov A.L., Belousov D.Yu. Ethical aspects of using software with artificial intelligence technology. Good Clinical Practice. 2021; (1): 70-84. (in Russian)]. https://dx.doi.org/ 10.37489/2588-0519-2021-1-70-84.
- Сахибгареева М.В., Заозерский А.Ю. Разработка системы прогнозирования диагнозов заболеваний на основе искусственного интеллекта. Вестник РГМУ. 2017; 6: 42-6. [Sakhibgareeva M.V., Zaozersky A.Yu. Developing an artificial intelligence-based system for medical prediction. Vestnik RGMU. 2017; (6): 42-6. (in Russian)].
- Кобякова О.С., Стародубов В.И., Кадыров Ф.Н., Обухова О.В., Ендовицкая Ю.В., Базарова И.Н., Чилилов А.М. Новая система договоров в рамках ОМС. Менеджер здравоохранения. 2021; 4: 76-82. [Kobyakova O.S., Starodubov V.I., Kadyrov F.N., Obukhova O.V., Endovitskaya Yu.V., Bazarova I.N., Chililov A.M. New system of contracts within the framework of compulsory health insurance. Manager Zdravoohranenia. 2021; (4): 76-82. (in Russian)]. https://dx.doi.org/10.21045/1811-0185-2021-4-76-82.
- Barnett-Itzhaki Z., Elbaz M., Butterman R., Amar D., Amitay M., Racowsky C. et al. Machine learning vs. classic statistics for the prediction of IVF outcomes. J. Assist. Reprod. Genet. 2020; 37(10): 2405-12. https://dx.doi.org/10.1007/s10815-020-01908-1.
- Wang Q.Q., Yu S.C., Qi X., Hu Y.H., Zheng W.J., Shi J.X., Yao H.Y. Overview of logistic regression model analysis and application. Zhonghua Yu Fang Yi Xue Za Zhi. 2019; 53(9): 955-60. https://dx.doi.org/10.3760/ cma.j.issn.0253-9624.2019.09.018.
- Uddin S., Khan A., Hossain M.E., Moni M.A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 2019; 19(1): 281. https://dx.doi.org/10.1186/s12911-019-1004-8.
- Jaworski M., Duda P., Rutkowski L., Jaworski M., Duda P., Rutkowski L. et al. New splitting criteria for decision trees in stationary data streams. IEEE Trans. Neural Netw. Learn. Syst. 2018; 29(6): 2516-29.
- Hu J., Szymczak S. A review on longitudinal data analysis with random forest. Brief. Bioinform. 2023; 24(2): bbad002. https://dx.doi.org/10.1093/bib/bbad002.
- Драпкина Ю.С., Макарова Н.П., Татаурова П.Д., Калинина Е.A. Поддержка врачебных решений с помощью глубокого машинного обучения при лечении бесплодия методами вспомогательных репродуктивных технологий. Медицинский cовет. 2023; 15: 27-37. [Drapkina J.S., Makarova N.Р., Tataurova P.D., Kalinina E.A. Deep machine learning applied to support clinical decision-making in the treatment of infertility using assisted reproductive technologies. Medical Council. 2023; (15): 27-37. (in Russian)]. https://dx.doi.org/10.21518/ms2023-368.
- Shen C., Wang Q., Priebe C.E. One-hot graph encoder embedding. IEEE Trans. Pattern Anal. Mach. Intell. 2023; 45(6): 7933-8. https://dx.doi.org/10.1109/TPAMI.2022.3225073.
- Гусев А.В. Перспективы нейронных сетей и глубокого машинного обучения в создании решений для здравоохранения. Врач и информационные технологии. 2017; 3: 92-105. [Gusev A.V. Prospects for neural networks and deep machine learning in creating health solutions. Information Technologies for the Physician. 2017; (3): 92-105. (in Russian)].
- Nayarisseri A., Khandelwal R., Tanwar P., Madhavi M., Sharma D., Thakur G. et al. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery. Curr. Drug Targets. 2021; 22(6): 631-55. https://dx.doi.org/10.2174/1389450122999210104205732.
- Wang C.W., Kuo C.Y., Chen C.H., Hsieh Y.H., Su E.C.Y. Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. PloS One. 2022; 17(6): e0267554. https://dx.doi.org/10.1371/journal.pone.0267554.
- Vaegter K.K., Lakic T.G., Olovsson M., Berglund L., Brodin T., Holte J. Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF/ICSI single-embryo transfers. Fertil. Steril. 2017; 107(3): 641-648.e2. https://dx.doi.org/10.1016/j.fertnstert.2016.12.005.
- Tarín J.J., Pascual E., García-Pérez M.A., Gómez R., Hidalgo-Mora J.J., Cano A. A predictive model for women's assisted fecundity before starting the first IVF/ICSI treatment cycle. J. Assist. Reprod. Genet. 2020; 37(1): 171-80. https://dx.doi.org/10.1007/s10815-019-01642-3.
- Yang H., Liu F., Ma Y., Di M. Clinical pregnancy outcomes prediction in vitro fertilization women based on random forest prediction model: A nested case-control study. Medicine (Baltimore). 2022; 101(49): e32232. https://dx.doi.org/10.1097/MD.0000000000032232.
- Zmuidinaite R., Sharara F.I., Iles R.K. Current advancements in noninvasive profiling of the Embryo Culture Media Secretome. Int. J. Mol. Sci. 2021; 22(5): 2513. https://dx.doi.org/10.3390/ijms22052513.
- Долудин Ю.В., Драпкина Ю.С., Сазонкина П.О. Киселев А.Р., Горбунов К.С. Виртуальная система хранения биологических образцов и ассоциированных данных. Свидетельство о государственной регистрации программы для ЭВМ. Номер свидетельства: RU 2023610092. Патентное ведомство: Россия. Год публикации: 2023. Номер заявки: 2022686282. Дата регистрации: 19.12.2022. [Doludin Yu.V., Drapkina Yu.S., Sazonkina P.O. Kiselev A.R., Gorbunov K.S. Virtual storage system for biological samples and associated data. Certificate of state registration of a computer program. Certificate number: RU 2023610092. Patent Office: Russia. Year of publication: 2023. Application number: 2022686282. Registration date: 19/12/2022. (in Russian)].
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