Predicting in vitro fertilization success in the Brazilian public health system: a machine learning approach

• Published in: Medical & biological engineering & computing Vol. 60; no. 7; pp. 1851 - 1861
• Main Authors: C. N. Barreto, Nayara, Castro, Giulia Z., Pereira, Ramon G., Pereira, Francisco A. N., Reis, Fernando M., Junior, Wagner M., Cavallo, Ines K. D., Gomes, Karina B.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-07-2022; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer Applications; Health services; Human Physiology; Imaging; Implantation; In vitro fertilization; Infertility; Learning algorithms; Machine learning; Original Article; Pregnancy; Public health; Radiology; Random variables; Success; Support vector machines; IVF; Infertility; FIV/ICSI; Prediction model; Machine learning; Random Forest
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-022-02569-1

Infertility has become a global health problem, increasing the number of couples looking for in vitro fertilization (IVF). Despite advances and technical improvements, some couples remain childless due to the high complexity of the technique. The use of machine learning (ML) in the prediction of pregnancy, computing factors that could interfere in the effectiveness of the treatment, is an important tool to optimize these factors and reach the success of pregnancy. The aim of this study was to apply ML models to determine variables related to pregnancy after IVF in a public health service, including pre-implantation variables. This study included 771 women who underwent IVF treatment at Hospital das Clínicas, Federal University of Minas Gerais, between 2013 and 2019. We used the following Machine Learning algorithms: Logistic Regression, Random Forest, XG Boost and Support Vector Machines. The Random Forest algorithm achieved the best performance, with better accuracy, sensitivity and area under the ROC curve to predict the success of IVF evaluated by pregnancy frequency. We also trained a specific model only for women older than 35 years old.
Graphical abstract
Variables in the Random Forest model related to pregnancy after in vitro fertilization