DOI : https://doi.org/10.18517/ijaseit.15.1.20498

Generative AI-Powered Synthetic Data for Enhancing Predictive Analytics in Blood Donation Supply Management: A Comparative Study of Machine Learning Models

Koh Chee Hong (1), Thong Chee Ling (2), Raenu AL Kolandaisamy (3), Abdul Samad Bin Shibghatullah (4), Nazirul Nazrin Bin Shahrol Nidzam (5), Samer Sarsam (6), Halimah Badioze Zaman (7)
(1) Institute of Computer Science & Digital Innovation (ICSDI), UCSI University, Kuala Lumpur, Malaysia
(2) Institute of Computer Science & Digital Innovation (ICSDI), UCSI University, Kuala Lumpur, Malaysia
(3) Institute of Computer Science & Digital Innovation (ICSDI), UCSI University, Kuala Lumpur, Malaysia
(4) College of Computing & Informatics, Universiti Tenaga Nasional, Putrajaya Campus, Selangor, Malaysia
(5) Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Putrajaya Campus, Selangor, Malaysia
(6) Faculty of Business & Law, Coventry University, Priory St, Coventry, United Kingdom
(7) Tan Sri Leo Moggie Distinguished Chair in Energy Informatics, IICE, Universiti Tenaga Nasional (UNITEN), Malaysia
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K. C. Hong, “Generative AI-Powered Synthetic Data for Enhancing Predictive Analytics in Blood Donation Supply Management: A Comparative Study of Machine Learning Models”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 9–19, Feb. 2025.
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Maintaining a sufficient and timely blood supply is an urgent and critical challenge in public health, where even minor miscalculations can lead to life-threatening shortages. This study evaluates the performance of machine learning models to improve blood donation forecasting. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) generated synthetic datasets that mirror real-world donation patterns to address data scarcity and variability issues. Leveraging transactional data from the Blood Bank Information System (BBISv2), a blood tracking system used by 22 main blood collection sites under the Ministry of Health (MoH) in Malaysia, 50 synthetic datasets were created and validated to ensure consistency with real data. The synthetic data showed minimal deviations from real data across key metrics, including mean (differences under 10%), variance (1 to 2 units), and skewness and kurtosis (0.03 or less). Among the models, the Random Forest algorithm demonstrated the highest performance, achieving an accuracy of 98.7%, a precision of 0.91, and an Area Under the Receiver Operating Characteristic (AUC-ROC) score of 0.92, making it the most reliable for predicting blood donation rates. Linear Regression also performed well, with an accuracy of 98.6%, while Neural Networks and Support Vector Machines (SVM) showed lower performance. This research provides a valuable tool for optimizing blood donation strategies, particularly in scenarios where real data is limited. Integrating validated synthetic data offers a novel approach for enhancing resource management in healthcare, ensuring reliable blood supply during high-demand periods.

R. Sethuraman, S. Murugan, and M. Saravanan, “AI and Machine Learning in Supply Chain Optimization: Mapping the Territory,” Blockchain, IoT, and AI Technologies for Supply Chain Management, pp. 177–220, 2024, doi: 10.1007/979-8-8688-0315-4_7.

P. Maheshwari, S. S. Kamble, A. Belhadi, S. Gupta, and S. K. Mangla, “Resilient healthcare network for simultaneous product allocations during supply chain disruptions,” Supply Chain Forum: An International Journal, vol. 25, no. 4, pp. 407–427, Aug. 2023, doi:10.1080/16258312.2023.2238669.

J. D. VanVactor, “Healthcare supply chain resiliency,” Journal of Supply Chain Management, Logistics and Procurement, vol. 3, no. 2, p. 148, Dec. 2020, doi: 10.69554/kugd8447.

K. A. Clauson, E. A. Breeden, C. Davidson, and T. K. Mackey, “Leveraging Blockchain Technology to Enhance Supply Chain Management in Healthcare:,” Blockchain in Healthcare Today, Mar. 2018, doi: 10.30953/bhty.v1.20.

B. Scala and C. F. Lindsay, “Supply chain resilience during pandemic disruption: evidence from healthcare,” Supply Chain Management: An International Journal, vol. 26, no. 6, pp. 672–688, May 2021, doi:10.1108/scm-09-2020-0434.

S.-W. Lin, K. N. Anisa, and Y.-C. Chen, “Effects of meteorological factors on blood donation,” Transfusion and Apheresis Science, vol. 59, no. 6, p. 102901, Dec. 2020, doi: 10.1016/j.transci.2020.102901.

K. Intharanut, P. Nimnuch, W. Naiwijarn, W. Pimsiri, O. Khantisitthiporn, and O. Nathalang, “Characteristics of Donors and Modelling of the Characteristics to Possible Forecast the Repeat Donors Profile at Thammasat University Hospital,” Indian Journal of Hematology and Blood Transfusion, vol. 39, no. 1, pp. 146–150, Aug. 2022, doi: 10.1007/s12288-022-01552-y.

T. Ahamed, Md. Nazrul Islam, S. M. Taslim Uddin Raju, and M. M. A. Hashem, “Blood Donor Arrival Forecasting Using Regression Model and Analysis of Donor Behavioural Pattern,” 2022 25th International Conference on Computer and Information Technology (ICCIT), pp. 897–902, Dec. 2022, doi:10.1109/iccit57492.2022.10054710.

H. Shih and S. Rajendran, “Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation’s Blood Supply,” Journal of Healthcare Engineering, vol. 2019, pp. 1–6, Sep. 2019, doi: 10.1155/2019/6123745.

A. Thijsen, C. N. Gemelli, T. E. Davison, and B. Masser, “Using the Health Action Process Approach to predict blood donation intentions and return behavior following a vasovagal reaction for whole blood and plasma donors,” Transfusion, vol. 62, no. 9, pp. 1791–1798, Aug. 2022, doi: 10.1111/trf.17052.

A. Adhikari, R. Joshi, and S. Basu, “Collaboration and coordination strategies for a multi-level AI-enabled healthcare supply chain under disaster,” International Journal of Production Research, vol. 63, no. 2, pp. 497–523, Sep. 2023, doi: 10.1080/00207543.2023.2252933.

A. Kumar, V. Mani, V. Jain, H. Gupta, and V. G. Venkatesh, “Managing healthcare supply chain through artificial intelligence (AI): A study of critical success factors,” Computers & Industrial Engineering, vol. 175, p. 108815, Jan. 2023, doi:10.1016/j.cie.2022.108815.

N. A. Alhamda and A. Z. Rahman, “The Impact of IoT Supply Chain Integrated Blockchain Technology Advancements in the Healthcare Sector: A Literature Review,” Journal of Logistics and Supply Chain, vol. 2, no. 1, pp. 33–40, Apr. 2022, doi: 10.17509/jlsc.v2i1.62835.

P. Yu, H. Xu, X. Hu, and C. Deng, “Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration,” Healthcare, vol. 11, no. 20, p. 2776, Oct. 2023, doi:10.3390/healthcare11202776.

A. Chansamut, “A Digital System Model for Healthcare Management in Thai Supply Chain,” International Journal of Supply Chain Management, vol. 12, no. 2, pp. 33–36, Apr. 2023, doi:10.59160/ijscm.v12i2.6127.

S. Reddy, “Generative AI in healthcare: an implementation science informed translational path on application, integration and governance,” Implementation Science, vol. 19, no. 1, Mar. 2024, doi:10.1186/s13012-024-01357-9.

M. Krishnan and A. Khastgir, “Enhancing Supply Chain Efficiency to Build Next-Gen Artificial Intelligence (AI)/Machine Learning Network Through Al-Driven Forecasting,” International Journal of Supply Chain Management, vol. 13, no. 3, pp. 28–41, Jun. 2024, doi:10.59160/ijscm.v13i3.6244.

A. Longaray, N. Marube, L. Ensslin, A. Dutra, and S. Ensslin, “Performance evaluation of public healthcare supply chain management: a critical literature review,” International Journal of Healthcare Technology and Management, vol. 1, no. 1, p. 1, 2022, doi:10.1504/ijhtm.2022.10054715.

J. Haw, K. Holloway, and M. Goldman, “How do we forecast tomorrow’s transfusion? Applying social science approaches to meet tomorrow’s transfusion needs: Blood donors and donation,” Transfusion Clinique et Biologique, vol. 30, no. 1, pp. 47–51, Feb. 2023, doi: 10.1016/j.tracli.2022.11.001.

S. Pahune and N. Rewatkar, “Healthcare: A Growing Role for Large Language Models and Generative AI,” International Journal for Research in Applied Science and Engineering Technology, vol. 11, no. 8, pp. 2288–2301, Aug. 2023, doi: 10.22214/ijraset.2023.55573.

T. Bou Assi, A. Haddad, L. Haddad, and O. Garraud, “Can a decentralized blood supply system reach 100% voluntary nonremunerated donation?,” The International Journal of Health Planning and Management, vol. 33, no. 4, Jul. 2018, doi:10.1002/hpm.2576.

S. K. Thakur, “Forecasting demand for blood products: Towards inventory management of a perishable product,” Bioinformation, vol. 20, no. 1, pp. 20–28, Jan. 2024, doi: 10.6026/973206300200020.

M. D. Wood, “Director General Joint Supply Chain, Defence Equipment and Support, 2003–08,” Who’s Who, Dec. 2007, doi:10.1093/ww/9780199540884.013.10000276.

A. K. Gupta, G. V. Awatade, S. S. Padole, and Y. S. Choudhari, “Digital Supply Chain Management Using AI, ML and Blockchain,” Innovative Supply Chain Management via Digitalization and Artificial Intelligence, pp. 1–19, 2022, doi: 10.1007/978-981-19-0240-6_1.

A. J. Scriffignano, “From supply chain to integrated value chain : Re-envisioning counterparty relationships in the context of AI and the convergence of disruption,” Journal of Supply Chain Management, Logistics and Procurement, vol. 1, no. 4, p. 334, Jun. 2019, doi:10.69554/xbnr3835.

K. I. Afrashtehfar and A. S. H. Abu-Fanas, “Metaverse, Crypto, and NFTs in Dentistry,” Education Sciences, vol. 12, no. 8, p. 538, Aug. 2022, doi: 10.3390/educsci12080538.

J. Nsikan, E. A. Affiah, I. Briggs, and N. Koko, “Sustainable supplier selection factors and supply chain performance in the Nigerian healthcare industry,” Journal of Transport and Supply Chain Management, vol. 16, Apr. 2022, doi: 10.4102/jtscm.v16i0.633.

N. Labib and S. Gharib, “Healthcare in the Era of Generative AI,” Industry 5.0 for Smart Healthcare Technologies, pp. 265–274, Jul. 2024, doi: 10.1201/9781032632223-22.

M. Patel Rohit Malik, “The Disruptive Influence of Generative AI in Life Science and Healthcare,” International Journal of Science and Research (IJSR), vol. 13, no. 4, pp. 216–219, Apr. 2024, doi: 10.21275/sr24402184343.

T. Hashizume et al., "Development and validation of a scoring system to predict vasovagal reaction upon whole blood donation," Vox Sanguinis, vol. 119, no. 4, pp. 300–307, Dec. 2023, doi:10.1111/vox.13579.

N. Al-Ma’aitah, “Investigating the interplay between supply chain agility, human capital and supply chain performance in the healthcare sector of Jordan,” Uncertain Supply Chain Management, vol. 12, no. 2, pp. 751–760, 2024, doi: 10.5267/j.uscm.2024.1.011.

S. Selvaraj, “Empowering Patients with AI-Driven Personalized Care: The Transformative Power of Generative AI and Healthcare Data Integration,” International Journal of Science and Research (IJSR), vol. 13, no. 7, pp. 337–343, Jul. 2024, doi: 10.21275/sr24703063340.

R. Malik and K. Naudiyal, “Enabling Generative AI for Life Sciences and Healthcare Customers using the Power of Cloud,” International Journal of Science and Research (IJSR), vol. 12, no. 11, pp. 1356–1360, Nov. 2023, doi: 10.21275/sr231115115845.

O. Lemon, “Introducing generative AI into healthcare practice,” The Journal for The Foundation of Science and Technology, vol. 23, no. 7, Mar. 2024, doi: 10.53289/ovfa7327.

S. Y. W. Chai, F. J. F. Phang, L. S. Yeo, L. H. Ngu, and B. S. How, “Future era of techno-economic analysis: Insights from review,” Frontiers in Sustainability, vol. 3, Aug. 2022, doi:10.3389/frsus.2022.924047.

V. Yandrapalli, “Revolutionizing Supply Chains Using Power of Generative AI,” International Journal of Research Publication and Reviews, vol. 4, no. 12, pp. 1556–1562, Dec. 2023, doi:10.55248/gengpi.4.1223.123417.

J. C. Essila, “Strategies for reducing healthcare supply chain inventory costs,” Benchmarking: An International Journal, vol. 30, no. 8, pp. 2655–2669, Aug. 2022, doi: 10.1108/bij-11-2021-0680.

K. Lee, A. F. Cooper, and J. Grimmelmann, “Talkin’ ‘Bout AI Generation: Copyright and the Generative AI Supply Chain,” SSRN Electronic Journal, 2023, doi: 10.2139/ssrn.4523551.

A. Reena Dhan and B. Kumar, “Machine Learning for Healthcare: Predictive Analytics and Personalized Medicine,” International Journal of Science and Research (IJSR), vol. 13, no. 6, pp. 1307–1313, Jun. 2024, doi: 10.21275/mr24608013906.

Y. R. Venugopal and S. V., "Ensemble Learning Approaches for Improved Predictive Analytics in Healthcare," International Journal of Research Publication and Reviews, vol. 5, no. 3, pp. 757–760, Mar. 2024, doi: 10.55248/gengpi.5.0324.0629.

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