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
  • [21] Predicting Resource Utilization for Cloud Workloads Using Machine Learning Techniques
    Adane, Padma D.
    Kakde, O. G.
    PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT), 2018, : 1372 - 1376
  • [22] Predicting Suicidal Behaviors in Individuals With Diabetes Using Machine Learning Techniques
    Mamun, Mohammed A.
    Al-Mamun, Firoj
    Hasan, Md Emran
    Roy, Nitai
    ALmerab, Moneerah Mohammad
    Muhit, Mohammad
    Moonajilin, Mst. Sabrina
    PERSPECTIVES IN PSYCHIATRIC CARE, 2024, 2024
  • [23] Predicting dairy cattle heat stress using machine learning techniques
    Becker, C. A.
    Aghalari, A.
    Marufuzzaman, M.
    Stone, A. E.
    JOURNAL OF DAIRY SCIENCE, 2021, 104 (01) : 501 - 524
  • [24] Predicting the Outcome of Patients With Subarachnoid Hemorrhage Using Machine Learning Techniques
    de Toledo, Paula
    Rios, Pablo M.
    Ledezma, Agapito
    Sanchis, Araceli
    Alen, Jose F.
    Lagares, Alfonso
    IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2009, 13 (05): : 794 - 801
  • [25] Data Balancing Techniques for Predicting Student Dropout Using Machine Learning
    Mduma, Neema
    DATA, 2023, 8 (03)
  • [26] Predicting Academic Success of College Students Using Machine Learning Techniques
    Guanin-Fajardo, Jorge Humberto
    Guana-Moya, Javier
    Casillas, Jorge
    DATA, 2024, 9 (04)
  • [27] Predicting genetic merit in Harnali sheep using machine learning techniques
    Spandan Shashwat Dash
    Yogesh C. Bangar
    Ankit Magotra
    C. S. Patil
    Tropical Animal Health and Production, 2025, 57 (5)
  • [28] Predicting exclusive breastfeeding in maternity wards using machine learning techniques
    Oliver-Roig, Antonio
    Ramon Rico-Juan, Juan
    Richart-Martinez, Miguel
    Cabrero-Garcia, Julio
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 221
  • [29] Predicting and Reducing Dropout in Virtual Learning using Machine Learning Techniques: A Systematic Review
    Tamada, Mariela Mizota
    de Magalhaes Netto, Jose Francisco
    de Lima, Dhanielly Paulina R.
    2019 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE 2019), 2019,
  • [30] Towards Predicting Student's Dropout in University Courses Using Different Machine Learning Techniques
    Kabathova, Janka
    Drlik, Martin
    APPLIED SCIENCES-BASEL, 2021, 11 (07):
12345