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Predicting Blood Donors Using Machine Learning Techniques

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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.

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Acknowledgements

Xiao Qin’s work is supported by the U.S. National Science Foundation under Grants IIS-1618669, OAC-1642133, CCF-0845257.

Author information

Authors and Affiliations

  1. Computer Science and Software Engineering, Samuel Ginn College of Engineering, Auburn University, Auburn, AL, 36849, USA

    Christian Kauten & Xiao Qin

  2. Department of Systems & Technology, Harbert College of Business, Auburn University, Auburn, AL, 36849, USA

    Ashish Gupta

  3. Department of Supply Chain Management, Harbert College of Business, Auburn University, Auburn, AL, 36849, USA

    Glenn Richey

Authors
  1. Christian Kauten
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  2. Ashish Gupta
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  3. Xiao Qin
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  4. Glenn Richey
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Corresponding author

Correspondence to Ashish Gupta.

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Appendices

Appendix A: Symbols and Annotations

Table 5 Symbols and annotations
Full size table

Appendix B: Dataset Features

Table 6 Dataset Features: The engineered features of the donor retention dataset
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Fig. 4
figure 4

A heatmap visualization of the correlation matrix of the engineered dataset described by Table 6. The color (luminance) of a cross-section, as well as the area, measures the Pearson correlation between variables in the dataset

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Fig. 5
figure 5

Histogram plots illustrating the distributions of variables in the dataset

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Glossary

BC

Blood Center (Name blinded)

ARC

The American Red Cross

ANN

Artificial Neural Network

CART

Classification & Regression Trees

CBA

Classification Based Association

CDC

The Center for Disease Control

DRG

Diagnosis Related Groups

FEMA

The Federal Emergency Management Agency

FDA

The Food and Drug Administration

GB

Gradient Boosting

IRCS

Indian Red Cross Society

k NN

k-Nearest Neighbors

LDA

Linear Discriminant Analysis

MCC

Matthew’s correlation coefficient

NBCUS

The National Blood Collection and Utilization Survey

PBM

Patient Blood Management

RBC

Red Blood Cell

RF

Random Forest

RFM

Recency, Frequency, and Monetary Value

SMOTE

Synthetic Minority Oversampling Technique

SVM

Support Vector Machine

UCI

University of California, Irvine

US

United States

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Kauten, C., Gupta, A., Qin, X. et al. Predicting Blood Donors Using Machine Learning Techniques. Inf Syst Front 24, 1547–1562 (2022). https://doi.org/10.1007/s10796-021-10149-1

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  • DOI: https://doi.org/10.1007/s10796-021-10149-1

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