Departamento de Análises Clínicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil.
Programa de Pós-Graduação em Engenharia Elétrica, Faculdade de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
Med Biol Eng Comput. 2022 Jul;60(7):1851-1861. doi: 10.1007/s11517-022-02569-1. Epub 2022 May 4.
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. Variables in the Random Forest model related to pregnancy after in vitro fertilization.
不孕已成为全球性健康问题，越来越多的夫妇寻求体外受精（IVF）。尽管技术取得了进步和改进，但由于技术的高度复杂性，一些夫妇仍然无法生育。机器学习（ML）在预测妊娠、计算可能影响治疗效果的因素方面的应用，是优化这些因素并实现妊娠成功的重要工具。本研究旨在将 ML 模型应用于在公共卫生服务中确定与 IVF 后妊娠相关的变量，包括植入前变量。这项研究包括了 771 名在 2013 年至 2019 年期间在米纳斯吉拉斯联邦大学临床医院接受 IVF 治疗的女性。我们使用了以下机器学习算法：逻辑回归、随机森林、XG Boost 和支持向量机。随机森林算法的性能最好，具有更好的准确性、敏感性和 ROC 曲线下面积，以预测通过妊娠频率评估的 IVF 成功。我们还专门为年龄大于 35 岁的女性训练了一个模型。与体外受精后妊娠相关的随机森林模型中的变量。
