Predicting Blood Donors Using Machine Learning Techniques
Christian Kauten (),
Ashish Gupta (),
Xiao Qin () and
Glenn Richey ()
Additional contact information
Christian Kauten: Auburn University
Ashish Gupta: Auburn University
Xiao Qin: Auburn University
Glenn Richey: Auburn University
Information Systems Frontiers, 2022, vol. 24, issue 5, No 11, 1547-1562
Abstract:
Abstract 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.
Keywords: Analytics; Blood donors; Blood supply; Machine learning; Retention (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s10796-021-10149-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:24:y:2022:i:5:d:10.1007_s10796-021-10149-1
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796
DOI: 10.1007/s10796-021-10149-1
Access Statistics for this article
Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao
More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().