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在单个试管婴儿周期中,使用比较机器学习算法预测高比例的三/多个前核 (3PN/MPN) 异构体的风险

Pingping Hong¹, Yaxin Lu², Haiyang Li¹

¹Reproductive Medicine Center, The Third Affiliated Hospital of Sun Yat-sen University, 6# East Longkou Road, Gangding, Tianhe District, Guangzhou 510630 China.

European journal of obstetrics, gynecology, and reproductive biology

|January 18, 2025

概括

机器学习可以预测试管婴儿周期中异常卵巢的高率. 该AdaBoost算法确定了关键预测因素,如年龄和卵细胞计数,可能防止多精子症.

科学领域:

辅助生殖技术是一种辅助生殖技术.
机器学习在医疗保健中的应用
胚胎学胚胎学

背景情况:

大多数辅助生殖中的机器学习都专注于怀孕预测.
这项研究探讨了在试管婴儿周期中预测高比例 (>30%) 的三个前核/多前核 (3PN/MPN) 胚胎.

研究的目的:

确定最有效的机器学习模型来预测高的3PN/MPN阴茎比例.
了解与增加的3PN/MPN胚胎细胞数量相关的临床和生物因素.

主要方法:

包括AdaBoost和Gaussian Naive Bayes在内的八个机器学习算法被训练和验证.
使用了2015-2020年试管婴儿周期的数据,将超过30%的3PN/MPN阴囊细胞的周期归类为高比例.

主要成果:

在模型构建和验证方面,AdaBoost表现出卓越的性能.
确定的主要预测因素包括年龄,FSH,E2,LH,AFC,AMH,卵细胞产量和男性因子不孕症.
某些激素的较高水平和较低的AFC/AMH与3PN/MPN率的增加有关.

结论:

机器学习,特别是AdaBoost,可以在授精前预测高3PN/MPN胞胎比例的风险.
这种预测能力可以通过引导ICSI等受精方法来帮助避免多精子症.

关键词:

不正常的受精不正常的受精.在AdaBoost算法中,我们使用的是AdaBoost算法.在这个过程中,IVF IVF.机器学习算法机器学习算法三个/多个前核 (3PN/MPN) 的生殖细胞.

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