IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v24y2022i5d10.1007_s10796-021-10149-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-021-10149-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-021-10149-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abdulaziz Ahmed & Mohammed Al-Maamari & Mohammad Firouz & Dursun Delen, 2024. "An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations," Information Systems Frontiers, Springer, vol. 26(5), pages 1893-1913, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Homaei, Shamim, 2020. "Toward a coordination of inventory and distribution schedules for blood in disasters," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    2. Martina Doneda & Semih Yalçındağ & Ettore Lanzarone, 2024. "A three-stage matheuristic for home blood donation appointment reservation and collection routing," Flexible Services and Manufacturing Journal, Springer, vol. 36(4), pages 1222-1252, December.
    3. Dehghani, Maryam & Abbasi, Babak & Oliveira, Fabricio, 2021. "Proactive transshipment in the blood supply chain: A stochastic programming approach," Omega, Elsevier, vol. 98(C).
    4. Nagurney, Anna & Dutta, Pritha, 2019. "Competition for blood donations," Omega, Elsevier, vol. 85(C), pages 103-114.
    5. Hosseini-Motlagh, Seyyed-Mahdi & Samani, Mohammad Reza Ghatreh & Cheraghi, Sara, 2020. "Robust and stable flexible blood supply chain network design under motivational initiatives," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    6. Sara Cheraghi & Seyyed-Mahdi Hosseini-Motlagh, 2020. "Responsive and reliable injured-oriented blood supply chain for disaster relief: a real case study," Annals of Operations Research, Springer, vol. 291(1), pages 129-167, August.
    7. Asadpour, Milad & Olsen, Tava Lennon & Boyer, Omid, 2022. "An updated review on blood supply chain quantitative models: A disaster perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    8. Kamyabniya, Afshin & Noormohammadzadeh, Zohre & Sauré, Antoine & Patrick, Jonathan, 2021. "A robust integrated logistics model for age-based multi-group platelets in disaster relief operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    9. Mohammad Reza Ghatreh Samani & Seyyed-Mahdi Hosseini-Motlagh, 2021. "A robust framework for designing blood network in disaster relief: a real-life case," Operational Research, Springer, vol. 21(3), pages 1529-1568, September.
    10. Yoon Sang Lee & Chulhwan Chris Bang, 2022. "Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network," Information Systems Frontiers, Springer, vol. 24(6), pages 1795-1809, December.
    11. Donya Rahmani, 2019. "Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions," Annals of Operations Research, Springer, vol. 283(1), pages 613-641, December.
    12. Samani, Mohammad Reza Ghatreh & Hosseini-Motlagh, Seyyed-Mahdi & Homaei, Shamim, 2020. "A reactive phase against disruptions for designing a proactive platelet supply network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    13. Chua, Geoffrey A. & Senga, Juan Ramon L., 2022. "Blood supply interventions during disasters: Efficiency measures and strategies to mitigate volatility," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    14. Semih Yalçındağ & Seda Baş Güre & Giuliana Carello & Ettore Lanzarone, 2020. "A stochastic risk-averse framework for blood donation appointment scheduling under uncertain donor arrivals," Health Care Management Science, Springer, vol. 23(4), pages 535-555, December.
    15. Lydia Bouzar-Benlabiod & Stuart H. Rubin, 2020. "Heuristic Acquisition for Data Science," Information Systems Frontiers, Springer, vol. 22(5), pages 1001-1007, October.
    16. Gilani Larimi, Niloofar & Yaghoubi, Saeed & Hosseini-Motlagh, Seyyed-Mahdi, 2019. "Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    17. Seyyed-Mahdi Hosseini-Motlagh & Niloofar Gilani Larimi & Maryam Oveysi Nejad, 2022. "A qualitative, patient-centered perspective toward plasma products supply chain network design with risk controlling," Operational Research, Springer, vol. 22(1), pages 779-824, March.
    18. Dillon, Mary & Vauhkonen, Ilmari & Arvas, Mikko & Ihalainen, Jarkko & Vilkkumaa, Eeva & Oliveira, Fabricio, 2023. "Supporting platelet inventory management decisions: What is the effect of extending platelets’ shelf life?," European Journal of Operational Research, Elsevier, vol. 310(2), pages 640-654.
    19. Saleh Y. Alghamdi, 2023. "A Review of Blood Delivery for Sustainable Supply Chain Management (BSCM)," Sustainability, MDPI, vol. 15(3), pages 1-21, February.
    20. Meneses, Maria & Santos, Daniel & Barbosa-Póvoa, Ana, 2023. "Modelling the Blood Supply Chain," European Journal of Operational Research, Elsevier, vol. 307(2), pages 499-518.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:24:y:2022:i:5:d:10.1007_s10796-021-10149-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.