Sadegh-Zadeh Seyed-Ali, Khanjani Sanaz, Javanmardi Shima, Bayat Bita, Naderi Zahra, Hajiyavand Amir M
Department of Computing, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, United Kingdom.
Department of Computer Engineering, Razi University, Kermanshah, Iran.
Front Artif Intell. 2024 Nov 5;7:1392611. doi: 10.3389/frai.2024.1392611. eCollection 2024.
This study addresses the research problem of enhancing Fertilization (IVF) success rate prediction by integrating advanced machine learning paradigms with gynecological expertise. The methodology involves the analysis of comprehensive datasets from 2017 to 2018 and 2010-2016. Machine learning models, including Logistic Regression, Gaussian NB, SVM, MLP, KNN, and ensemble models like Random Forest, AdaBoost, Logit Boost, RUS Boost, and RSM, were employed. Key findings reveal the significance of patient demographics, infertility factors, and treatment protocols in IVF success prediction. Notably, ensemble learning methods demonstrated high accuracy, with Logit Boost achieving an accuracy of 96.35%. The implications of this research span clinical decision support, patient counseling, and data preprocessing techniques, highlighting the potential for personalized IVF treatments and continuous monitoring. The study underscores the importance of collaboration between gynecologists and data scientists to optimize IVF outcomes. Prospective studies and external validation are suggested as future directions, promising to further revolutionize fertility treatments and offer hope to couples facing infertility challenges.
本研究旨在解决通过将先进的机器学习范式与妇科专业知识相结合来提高体外受精（IVF）成功率预测的研究问题。该方法涉及对2017年至2018年以及2010 - 2016年的综合数据集进行分析。使用了机器学习模型，包括逻辑回归、高斯朴素贝叶斯、支持向量机、多层感知器、K近邻，以及诸如随机森林、自适应增强、逻辑增强、RUS增强和RSM等集成模型。主要研究结果揭示了患者人口统计学特征、不孕因素和治疗方案在IVF成功预测中的重要性。值得注意的是，集成学习方法显示出高准确率，逻辑增强的准确率达到了96.35%。本研究的意义涵盖临床决策支持、患者咨询和数据预处理技术，突出了个性化IVF治疗和持续监测的潜力。该研究强调了妇科医生与数据科学家合作以优化IVF结果的重要性。前瞻性研究和外部验证被建议作为未来的方向，有望进一步彻底改变生育治疗，并为面临不孕挑战的夫妇带来希望。
