使用机器学习预测献血者回归行为.
Cheng-Hung Yeh1, Chi-Ming Hung1, Hao-Hsuan Chung2
1Taiwan Blood Services Foundation Kaohsiung Blood Center, Kaohsiung City, Taiwan, ROC.
Vox sanguinis
|October 20, 2025
概括
机器学习模型准确地预测了献血者的回报行为,通过精确营销显著提高了回报率. 这种方法有助于血液银行更有效地满足输血需求.
科学领域:
- 生物医学信息学 生物医学信息学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 评估献血者回血行为的传统方法依赖于问卷.
- 需要增强的捐赠者邀请模型,可以使用行为变量预测捐赠动态.
- 机器学习 (ML) 提供了一种有前途的方法来处理献血者数据集,以提高回报率.
研究的目的:
- 将机器学习纳入处理献血者数据集的过程.
- 开发一个增强的捐赠者邀请模型来预测捐赠动态.
- 通过精密营销,提高献血者回报率.
主要方法:
- 使用来自台湾高雄血液中心 (2022-2023) 的献血者记录进行了一项回顾性研究.
- 使用了三种机器学习模型 (MLM):极度梯度提升 (XGBoost),轻梯度提升机 (LightGBM) 和分类提升 (CatBoost).
- 模型进行了交叉验证并使用准确度,平均准确度,平衡准确度,F1得分和日志损失进行评估. 最好的模型生成了用于测试的预测性捐赠者返回列表.
主要成果:
- 这三个MLM都表现出强大的预测能力,CatBoost表现最好.
- 确定的主要影响因素是返回捐赠间隔和捐赠地点.
- 观察到捐赠者回归率显著改善 (57.99%与15.54%相比,p < 0.0001).
结论:
- 机器学习模型利用行为和人口统计学变量,可以有效地预测和识别高回报率的献血者.
- 基于这些预测的精确营销策略显著提高了捐赠者回报率.
- 这种方法使得血液银行能够更快地满足输血需求,特别是在紧急情况下.
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