Predicting Blood Donors Using Machine Learning Techniques

被引：8
|
作者：
Kauten, Christian [1 ]
Gupta, Ashish [2 ]
Qin, Xiao [1 ]
Richey, Glenn [3 ]
机构：
[1] Auburn Univ, Samuel Ginn Coll Engn, Comp Sci & Software Engn, Auburn, AL 36849 USA
[2] Auburn Univ, Harbert Coll Business, Dept Syst & Technol, Auburn, AL 36849 USA
[3] Auburn Univ, Harbert Coll Business, Dept Supply Chain Management, Auburn, AL 36849 USA
来源：
INFORMATION SYSTEMS FRONTIERS | 2022年 / 24卷 / 05期
基金：
美国国家科学基金会;
关键词：
Analytics; Blood donors; Blood supply; Machine learning; Retention; RETENTION; CELLS;
D O I：
10.1007/s10796-021-10149-1
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1547 / 1562
页数：16
相关论文
共 50 条
  • [1] Predicting Blood Donors Using Machine Learning Techniques
    Christian Kauten
    Ashish Gupta
    Xiao Qin
    Glenn Richey
    Information Systems Frontiers, 2022, 24 : 1547 - 1562
  • [2] Predicting IRI Using Machine Learning Techniques
    Sharma, Ankit
    Sachdeva, S. N.
    Aggarwal, Praveen
    INTERNATIONAL JOURNAL OF PAVEMENT RESEARCH AND TECHNOLOGY, 2023, 16 (01) : 128 - 137
  • [3] Predicting IRI Using Machine Learning Techniques
    Ankit Sharma
    S. N. Sachdeva
    Praveen Aggarwal
    International Journal of Pavement Research and Technology, 2023, 16 : 128 - 137
  • [4] Predicting Diabetes Using Machine Learning Techniques
    Kirgil, Elif Nur Haner
    Erkal, Begum
    Ayyildiz, Tulin Ercelebi
    2022 INTERNATIONAL CONFERENCE ON THEORETICAL AND APPLIED COMPUTER SCIENCE AND ENGINEERING (ICTASCE), 2022, : 137 - 141
  • [5] Predicting performance of swimmers using machine learning techniques
    Guerra-Salcedo, Cesar M.
    Janek, Libor
    Perez-Ortega, Joaquin
    Pazos-Rangel, Rodolfo A.
    WMSCI 2005: 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Vol 3, 2005, : 146 - 148
  • [6] Predicting Driver Destination using Machine Learning Techniques
    Manasseh, Christian
    Sengupta, Raja
    2013 16TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS - (ITSC), 2013, : 142 - 147
  • [7] Predicting bank insolvencies using machine learning techniques
    Petropoulos, Anastasios
    Siakoulis, Vasilis
    Stavroulakis, Evangelos
    Vlachogiannakis, Nikolaos E.
    INTERNATIONAL JOURNAL OF FORECASTING, 2020, 36 (03) : 1092 - 1113
  • [8] Predicting Employee Attrition Using Machine Learning Techniques
    Fallucchi, Francesca
    Coladangelo, Marco
    Giuliano, Romeo
    De Luca, Ernesto William
    COMPUTERS, 2020, 9 (04) : 1 - 17
  • [9] Predicting Students' Emotions Using Machine Learning Techniques
    Altrabsheh, Nabeela
    Cocea, Mihaela
    Fallahkhair, Sanaz
    ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 2015, 9112 : 537 - 540
  • [10] Predicting Software Anomalies using Machine Learning Techniques
    Alonso, Javier
    Belanche, Lluis
    Avresky, Dimiter R.
    2011 10TH IEEE INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA), 2011,
12345