The week's pick
Random Articles
Reseach Article
Predicting Blood Donor Retention with Light GBM: A High-Performance Gradient Boosting Framework
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 68 |
| Year of Publication: 2025 |
| Authors: Nahashon Kiarie, Mary Mwadulo, Amos Chege Kirongo |
10.5120/ijca2025924519
|
Nahashon Kiarie, Mary Mwadulo, Amos Chege Kirongo . Predicting Blood Donor Retention with Light GBM: A High-Performance Gradient Boosting Framework. International Journal of Computer Applications. 186, 68 ( Feb 2025), 24-28. DOI=10.5120/ijca2025924519
Abstract
Blood donation is critical for ensuring a stable and reliable supply of blood, yet blood donor retention remains a complex and persistent challenge. Previous attempts to develop predictive models for blood donor retention have often yielded relatively low accuracy and fail to address the class imbalance challenge that come with blood donation data, limiting their practical application in addressing this challenge. This study investigates the use of the Light Gradient Boosting Machine (LightGBM) as a high-performance gradient boosting framework for predicting blood donor retention. LightGBM employs a leaf-wise growth strategy, which significantly improves accuracy by minimizing loss at each iteration. It also supports histogram-based learning, reducing memory consumption and speeding up computation, making it suitable for the blood donation prediction. The study utilized data obtained from Kenya blood banks, consisting of 5000 records and nine features, to develop and evaluate the model. The LightGBM model achieved an accuracy of 98.3% and an F1 score of 97.8 which was higher as compared to the existing models. The results demonstrate that LightGBM is an effective and computationally efficient tool for predicting blood donor retention. Its ability to handle large, imbalanced datasets and complex patterns makes it well-suited for real-world applications in predictive analytics. This study provides blood agencies with a more reliable model for accurately predicting blood donor retention, reducing recruitment costs, and enabling targeted retention strategies to ensure a steady blood supply.
References
- A. Van Dongen, “Easy come, easy go. Retention of blood donors,” Transfusion Medicine, vol. 25, no. 4, pp. 227–233, Aug. 2015, doi: 10.1111/tme.12249.
- J. Ou-Yang, C.-H. Bei, B. He, and X. Rong, “Factors influencing blood donation: a cross-sectional survey in Guangzhou, China,” Transfusion Medicine, vol. 27, no. 4, pp. 256–267, Aug. 2017, doi: 10.1111/tme.12410.
- World Bank, “Ensuring Access to Safe Blood in Kenya Amid COVID-19 Pandemic.” Accessed: Jul. 08, 2023. [Online]. Available: https://www.worldbank.org/en/news/feature/2022/05/06/ensuring-access-to-safe-blood-in-kenya-enhanced-amid-covid-19-pandemic
- A. Panesar, Machine Learning and AI for Healthcare. Berkeley, CA: Apress, 2019. doi: 10.1007/978-1-4842-3799-1.
- M. Cloutier, Y. Grégoire, K. Choucha, A. Amja, and A. Lewin, “Prediction of donation return rate in young donors using machine‐learning models,” ISBT Sci Ser, vol. 16, no. 1, pp. 119–126, Feb. 2021, doi: 10.1111/voxs.12618.
- C. Marade, A. Pradeep, D. Mohanty, and C. Patil, “Forecasting Blood Donor Response Using Predictive Modelling Approach,” International Journal of Computer Science and Mobile Computing, vol. 8, no. 4, pp. 73–77, 2019.
- K. Pabreja and A. Bhasin, “A Predictive Analytics Framework for Blood Donor Classification,” International Journal of Big Data and Analytics in Healthcare, vol. 6, no. 2, pp. 1–14, Jul. 2021, doi: 10.4018/IJBDAH.20210701.oa1.
- N. H. A. Malek, W. F. W. Yaacob, Y. B. Wah, S. A. Md Nasir, N. Shaadan, and S. W. Indratno, “Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 1, p. 598, Jan. 2022, doi: 10.11591/ijeecs.v29.i1.pp598-608.
- Y. Dei-Adomakoh, L. Asamoah-Akuoko, B. Appiah, A. Yawson, and E. Olayemi, “Safe blood supply in sub-Saharan Africa: challenges and opportunities,” Lancet Haematol, vol. 8, no. 10, pp. e770–e776, Oct. 2021, doi: 10.1016/S2352-3026(21)00209-X.
- S. Liu et al., “Machine learning models to predict red blood cell transfusion in patients undergoing mitral valve surgery,” Ann Transl Med, vol. 9, no. 7, pp. 530–530, Apr. 2021, doi: 10.21037/atm-20-7375.
- World Health Organization, “Global status report on blood safety and availability,” 2017.
- S. Campagnini, C. Arienti, M. Patrini, P. Liuzzi, A. Mannini, and M. C. Carrozza, “Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review,” J Neuroeng Rehabil, vol. 19, no. 1, p. 54, Dec. 2022, doi: 10.1186/s12984-022-01032-4.
- W. B. Zulfikar, Y. A. Gerhana, and A. F. Rahmania, “An Approach to Classify Eligibility Blood Donors Using Decision Tree and Naive Bayes Classifier,” in 2018 6th International Conference on Cyber and IT Service Management (CITSM), IEEE, Aug. 2018, pp. 1–5. doi: 10.1109/CITSM.2018.8674353.
- P. Selvaraj, A. Sarin, and B. I. Seraphim, “Forecasting System for Donation of Blood Using SVM Model,” Int J Res Appl Sci Eng Technol, vol. 10, no. 5, pp. 136–140, May 2022, doi: 10.22214/ijraset.2022.41940.
- T. R. Mahesh et al., “AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease,” Comput Intell Neurosci, vol. 2022, pp. 1–11, Apr. 2022, doi: 10.1155/2022/9005278.
- S. Bates, T. Hastie, and R. Tibshirani, “Cross-Validation: What Does It Estimate and How Well Does It Do It?,” J Am Stat Assoc, pp. 1–12, May 2023, doi: 10.1080/01621459.2023.2197686.
- C. Salazar-Concha and P. Ramírez-Correa, “Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm,” Symmetry (Basel), vol. 13, no. 8, p. 1460, Aug. 2021, doi: 10.3390/sym13081460.
Index Terms
Keywords