DELVING INTO BLOOD TRANSFUSIONS DATA THROUGH DATA MINING: A STUDY OF MAIZBHANDARI SHAH EMDADIA BLOOD DONORS GROUP TO SELECT VOLUNTEER BLOOD DONORS EFFICIENTLY

Syed Irfanul Hoque; Md. Minhazul Abedin; Mohammad Sohel Chowdhury

doi:10.46281/aijmsr.v10i1.1308

Syed Irfanul Hoque Managing Trustee of Darul Irfan Research Institute, Bangladesh
Md. Minhazul Abedin Noakhali Science and Technology University, Bangladesh
Mohammad Sohel Chowdhury Noakhali Science and Technology University, Bangladesh

DOI: https://doi.org/10.46281/aijmsr.v10i1.1308

Keywords: Data Mining, Maizbhandar Blood Donation, Clustering, Classification, Prediction Model.

Abstract

The demand for blood transfusion is rising gradually. Therefore, volunteer blood donors are needed to save the lives of patients. There are lot of volunteer donors in every society. But, the main problem people face is finding such donors at the right time. To locate potential blood donors, we collected data from the Maizbhandari Shah Emdadia blood donors’ group, which consists of 700 active volunteer blood donors. This study aims to choose potential volunteer donors efficiently at the emergency time from the Maizbhandari Shah Emdadia blood donors’ group based on their past data. We have developed two models, namely descriptive and predictive models, using data mining techniques. The descriptive model analyzes data patterns and explores the donors’ behaviour. A data mining clustering algorithm was used to develop the descriptive model. The underlying factors of donors' intention to donate blood were identified and evaluated using this descriptive model. These factors were then utilized to develop the predictive model, which in turn assists to predict whether a donor will donate blood or not during an emergency. The findings of these two models will assist the clinical experts in locating potential volunteer blood donors within the shortest period and thus save valuable lives.

Author Biographies

Syed Irfanul Hoque, Managing Trustee of Darul Irfan Research Institute, Bangladesh

Managing Trustee of Darul Irfan Research Institute (DIRI) and Nayeb Sajjadah Nasheen of Maizbhandar Darbar Sharif, Fatikchari, Chattogram, Bangladesh

Md. Minhazul Abedin, Noakhali Science and Technology University, Bangladesh

Student of Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Bangladesh and Associate Member of DIRI, Chattogram, Bangladesh

Mohammad Sohel Chowdhury, Noakhali Science and Technology University, Bangladesh

Student of Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Bangladesh, and Associate Member of DIRI, Chattogram, Bangladesh

References

Adetayo Olaniyi, A., & Olufunto Adedotun, K. (2018). Artificial Intelligence in Aircraft Docking: The Fear of Reducing Ground Marshalling Jobs to Robots and Way-Out. American International Journal of Multidisciplinary Scientific Research, 1(2), 25–32. https://doi.org/10.46281/aijmsr.v1i2.185

Agarwal, K., Gupta, M., Gupta, K., Khan, A., & Nallakaruppan, M. K. (2019, March). Blood Transfusion System Using Data Mining Techniques and GRA. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 1143–1147. https://doi.org/10.1109/icaccs.2019.8728401

Alajrami, E., Abu-Nasser, B. S., Khalil, A. J., Musleh, M. M., Barhoom, A. M., & Naser, S. A. (2019). Blood donation prediction using artificial neural network. International Journal of Academic Engineering Research (IJAER), 3 (10), 1-7.

Alkahtani, S. A., & Jilani, M. (2019). Predicting Return Donor and Analyzing Blood Donation Time Series using Data Mining Techniques. International Journal of Advanced Computer Science and Applications, 10(8).

Al-Masum Molla, M. (2017, June 14). Number of blood donors on rise. Retrieved June 10, 2021, from https://www.thedailystar.net/backpage/number-blood-donors-rise-1419814

Ashoori, M., & Taheri, Z. (2013, August). Using clustering methods for identifying blood donors behavior. In 5th Iranian Conference on Electrical and Electronics Engineering (ICEEE2013) (pp. 4055-4057).

Bangladesh is still to meet the demand of safe blood supply. (2017, June 14). Retrieved June 11, 2021, from https://www.who.int/bangladesh/news/detail/14-06-2017-bangladesh-is-still-to-meet-the-demand-of-safe-blood-supply

Bhardwaj, A., Sharma, A., & Shrivastava, V. K. (2012). Data mining techniques and their implementation in blood bank sector–a review. International Journal of Engineering Research and Applications (IJERA), 2(4), 1303-1309.

Blood safety and availability. (2020, June 10). Retrieved June 11, 2021, from https://www.who.int/en/news-room/fact-sheets/detail/blood-safety-and-availability

Dutta, L., Maji, G., Ghosh, P., & Sen, S. (2018, October). An Integrated Blood Donation Campaign Management System. Advances in Intelligent Systems and Computing, 133–143. https://doi.org/10.1007/978-981-13-1540-4_14

Eligibility Requirements. (n.d.). Retrieved July 7, 2021, from https://www.redcrossblood.org/donate-blood/how-to-donate/eligibility-requirements.html

Fahad, A. A. A. (2019). Design and implementation of blood bank system using web services in cloud environment. International Journal of MC Square Scientific Research, 11(3), 09-16.

Gulinac, M., Dikov, D., & Velikova, T. (2020). Epidemiological and Morphological Characteristics of Urothelial Bladder Cancer in a Bulgarian and a French Sample of Patients. American International Journal of Multidisciplinary Scientific Research, 6(1), 1–5. https://doi.org/10.46281/aijmsr.v6i1.547

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1), 10–18. https://doi.org/10.1145/1656274.1656278

Hashim, S. A., Al-Madani, A. M., Al-Amri, S. M., Al-Ghamdi, A. M., & Nahla, B. S. B. (2014). Online Blood Donation Reservation and Management system In Jeddah. Life Science Journal, 11(8).

KIRCI, P., AKTAS, S., & SEVINC, B. (2020, June). Analyzing Blood Donation probabilities and number of possible donors. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 1–4. https://doi.org/10.1109/hora49412.2020.9152872

Lakshmi, B. V., & Kumar, B. R. (2018). Leveraging Technologies to Redefine Business: Technology Perspective. American International Journal of Multidisciplinary Scientific Research, 1(3), 1–3. https://doi.org/10.46281/aijmsr.v1i3.187

Sahariar Hasan Jiisun, M., Akter Rupa, R., Hussain Chowdhury, M., Mushrofa, H., & Hoque, M. R. (2019). Blood Donation Systems in Bangladesh: Problems and Remedy. International Journal of Business and Management, 14(8), 145. https://doi.org/10.5539/ijbm.v14n8p145

Serteva, D., Poryazova, E., & Velikova, T. (2019). Endometriosis Locations and Coexistence with other Uterine Conditions in a Bulgarian Sample of Patients. American International Journal of Multidisciplinary Scientific Research, 5(2), 5–9. https://doi.org/10.46281/aijmsr.v5i2.255

Tithila, K. K. (2019, June 14). Donate blood, save life. Retrieved June 18, 2021, from https://www.dhakatribune.com/bangladesh/event/2019/06/15/donate-blood-save-life

Velikova, T., Velikov, T., & Mihailov, G. (2018). Higher Serum Levels of Stromelisyn 2 (MMP10) but not Matrilysin (MMP7) in Patients with End-Stage Chronic Kidney Disease on Chroniodialysis. American International Journal of Multidisciplinary Scientific Research, 4(1), 17–21. https://doi.org/10.46281/aijmsr.v4i1.196

World Health Organization. (2010). Towards 100% voluntary blood donation: a global framework for action.

Yeh, I. C., Yang, K. J., & Ting, T. M. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36(3), 5866–5871. https://doi.org/10.1016/j.eswa.2008.07.018