Predicting Blood Donors Using Machine Learning Techniques

被引：0
|
作者：
Christian Kauten
Ashish Gupta
Xiao Qin
Glenn Richey
机构：
[1] Auburn University,Computer Science and Software Engineering, Samuel Ginn College of Engineering
[2] Auburn University,Department of Systems & Technology, Harbert College of Business
[3] Auburn University,Department of Supply Chain Management, Harbert College of Business
来源：
Information Systems Frontiers | 2022年 / 24卷
关键词：
Analytics; Blood donors; Blood supply; Machine learning; Retention;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The United States’ blood supply chain is experiencing market decline due to recent innovations in surgical practice, transfusion management, and hospital policy. These innovations strain US blood centers, resulting in cuts to surge capacities, consolidation, and reduced funding for research and outreach programs. In this study, we use data from a regional blood center to explore the application of contemporary machine learning algorithms for modeling donor retention. Such predictive models of donor retention can be used to design more cost effective donor outreach programs. Using data from a large US blood center paired with random forest classifiers, we are able to build a model of donor retention with a Mathews correlation of coefficient of 0.851.
引用
收藏
页码：1547 / 1562
页数：15
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