Academic Journal
Predicting Blood Donors Using Machine Learning Techniques.
| Title: | Predicting Blood Donors Using Machine Learning Techniques. |
|---|---|
| Authors: | Kauten, Christian, Gupta, Ashish, Qin, Xiao, Richey, Glenn |
| Source: | Information Systems Frontiers; Oct2022, Vol. 24 Issue 5, p1547-1562, 16p, 1 Diagram, 7 Charts, 4 Graphs |
| 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. [ABSTRACT FROM AUTHOR] |
| Subject Terms: | BLOOD donors, OUTREACH programs, RANDOM forest algorithms, TECHNOLOGICAL innovations, PREDICTION models, MACHINE learning |
| Geographic Terms: | UNITED States |
| Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| ISSN: | 13873326 |
| DOI: | 10.1007/s10796-021-10149-1 |
| Database: | Complementary Index |