使用后勤回归-ABC混合模型预测试管婴儿结果:关于补充剂关联的概念验证研究
Uğur Ejder1, Pınar Uskaner Hepsağ2
1Department of Information Technology, Adana Alparslan Türkeş Science and Technology University, Adana, Türkiye.
PloS one
|November 25, 2025
概括
一个新的混合机器学习模型提高了体外受精 (IVF) 结果的预测准确性. 这种方法也提高了对补充剂如omega-3和叶酸如何影响试管婴儿成功的理解,从而产生了新的研究假设.
科学领域:
- 生殖医学 生殖医学
- 人工智能的人工智能
- 生物统计学 生物统计学
背景情况:
- 机器学习 (ML) 越来越多地用于辅助生殖技术 (ART).
- 现有研究经常使用具有有限优化的常规算法.
- 在体外受精 (IVF) 中的预测建模可以通过先进的ML框架来增强.
研究的目的:
- 调查混合物流回归-人工蜂群 (LR-ABC) 框架,以提高试管婴儿结果的预测性能.
- 评估LR-ABC模型在确定补充剂使用与试管婴儿成功之间的关联方面的可解释性.
- 将LR-ABC混合模型的性能与传统的ML算法进行比较.
主要方法:
- 对162名接受试管婴儿的妇女进行了回顾性分析,使用了21个预处理的临床,人口和补充变量.
- 实施四个ML算法 (KNN,CT,SVM,RF) 和它们的LR-ABC混合对应.
- 使用5倍交叉验证进行评估,用于类不平衡的SMOTE,用于可解释性的LIME.
主要成果:
- 在所有测试的算法中,LR-ABC混合模型在所有测试的算法中始终表现出色 (例如,随机森林准确性从85.2%增加到91.36%).
- LIME的解释强调了欧米茄-3,叶酸和营养学家对个体IVF预测的影响.
- 该研究证明了LR-ABC在改善试管婴儿研究中的预测和解释性方面的方法潜力.
结论:
- 该LR-ABC混合模型显示,它有望提高试管婴儿研究中的预测准确性和可解释性.
- 补充剂和试管婴儿结果之间的确定的关联是产生假设的,需要进一步验证.
- 未来的研究需要更大的,多中心的数据集,详细的补充和饮食信息来证实研究结果.
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