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可解释的机器学习模型用于预测与从不同病因的丸中手术提取精子相关的临床怀孕:一项回顾性研究

Shun-Shun Cao¹, Xiao-Ming Liu², Bo-Tian Song²

¹Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.

|July 29, 2024

概括

机器学习准确地预测了手术精子采集后的临床怀孕. 关键因素包括女性年龄,丸体积和荷尔蒙水平,有助于患者咨询不孕症治疗.

科学领域:

生殖医学生殖医学
医疗保健中的机器学习
不孕症研究不孕症研究

背景情况:

由于各种原因的手术精子采集 (SSR) 与临床怀孕率之间的联系尚未得到充分证实.
预测SSR后的怀孕结果需要先进的分析方法.

研究的目的:

开发和解释一种机器学习 (ML) 模型,用于预测SSR后的临床怀孕.
为了确定影响怀孕成功的关键因素,使用夏普利添加式扩张 (SHAP).

主要方法:

对345对不育夫妇进行了SSR的细胞内精子注射 (ICSI) 的回顾性分析.
评价了6个ML模型,因为其卓越的性能而选择了极端梯度提升 (XGBoost).
用SHAP分析来解释XGBoost模型的预测和决策过程.

主要成果:

该XGBoost模型显示了高的预测准确度 (AUROC:0.858,准确度:79.71%).
女性年龄是最重要的预测因素,年轻的年龄与更高的怀孕概率有关.
增加怀孕可能性的其他因素包括较大的丸体积,非烟草使用,较高的AMH和较低的FSH水平.

结论:

该XGBoost模型提供SSR后临床怀孕的准确和可靠的预测在不同的病因.
这种ML工具可以帮助进行临床决策和为接受SSR的个体提供患者咨询.

关键词:

临床怀孕临床怀孕机器学习是机器学习.沙普利添加剂扩展法沙普利添加剂扩展法手术精子采集手术精子采集

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