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Predicting Blood Donors Using Machine Learning Techniques

Author

Listed:
  • Christian Kauten

    (Auburn University)

  • Ashish Gupta

    (Auburn University)

  • Xiao Qin

    (Auburn University)

  • Glenn Richey

    (Auburn University)

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.

Suggested Citation

  • Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:5:d:10.1007_s10796-021-10149-1
    DOI: 10.1007/s10796-021-10149-1
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    References listed on IDEAS

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    1. Andres F. Osorio & Sally C. Brailsford & Honora K. Smith, 2015. "A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7191-7212, December.
    2. Justin M. Johnson & Taghi M. Khoshgoftaar, 2020. "The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data," Information Systems Frontiers, Springer, vol. 22(5), pages 1113-1131, October.
    3. Babak Abbasi & Golnaz Vakili & Stuart Chesneau, 2017. "Impacts of Reducing the Shelf Life of Red Blood Cells: A View from Down Under," Interfaces, INFORMS, vol. 47(4), pages 336-351, August.
    4. Baş, Seda & Carello, Giuliana & Lanzarone, Ettore & Yalçındağ, Semih, 2018. "An appointment scheduling framework to balance the production of blood units from donation," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1124-1143.
    5. Bram Klievink & Bart-Jan Romijn & Scott Cunningham & Hans Bruijn, 2017. "Big data in the public sector: Uncertainties and readiness," Information Systems Frontiers, Springer, vol. 19(2), pages 267-283, April.
    6. Leipnitz, Sigrun & de Vries, Martha & Clement, Michel & Mazar, Nina, 2018. "Providing health checks as incentives to retain blood donors — Evidence from two field experiments," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 628-640.
    7. Afshin Kamyabniya & M. M. Lotfi & Mohsen Naderpour & Yuehwern Yih, 2018. "Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach," Information Systems Frontiers, Springer, vol. 20(4), pages 759-782, August.
    8. Ashish Gupta & Amit Deokar & Lakshmi Iyer & Ramesh Sharda & Dave Schrader, 2018. "Big Data & Analytics for Societal Impact: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(2), pages 185-194, April.
    9. Beliën, Jeroen & Forcé, Hein, 2012. "Supply chain management of blood products: A literature review," European Journal of Operational Research, Elsevier, vol. 217(1), pages 1-16.
    10. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
    11. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
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