Exploring Blood Donors’ Status Through Clustering: A Method to Improve the Quality of Services in Blood Transfusion Centers

Authors

  • Maryam Ashoori1 1- Dept. of Information Technology Engineering, School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran.
  • Shahriar Mohammadi2 2- Dept. of Information Technology Engineering, School of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
  • Hoda Sadat Hossieny Eivary3 3- Dept. of Computer Engineering, Azad University, Ferdos, Iran.

DOI:

https://doi.org/10.22100/jkh.v11i4.1525

Keywords:

Blood donors, Data mining, Decision tree, Clustering.

Abstract

Introduction: Urgent need for blood and lack of an alternative for it necessitates the presence of a model to assist doctors in providing the proper services for the donors and also the right management of blood transfusion centers. The present study is aimed at determining blood donors’ status.

Methods: Cross-sectional survey was applied in the present study through census. The population included the data extracted from blood transfusion center of Birjand from Khordad to Shahrivar 1392 which was provided as an Excel file by the direct visit of the researcher from the blood transfusion organization. In the present study, first, two-step clustering and then C50, C&R TREE, CHAID, and QUEST algorithms were executed to obtain the best ratio among different fields. Analysis was performed using Clementine12.0 software.

Results: The obtained accuracy for executing C50, C&R Tree, CHAID, and QUEST equals 0.9998, 0.9960, 0.9930, and 0.8913, respectively. The results of indices including sensitivity, Specificity, accuracy, precision, FM, GM, FPR, FNR, and ER for C50 are indicators of better performance of this algorithm compared to the other ones. Important variables in modeling are blood pressure label, blood donation status and temperature.

Conclusion: The proposed model helps us in predicting faster and more precise status of blood donation and also the proper management of the blood transfusion centers and it canbe and effive step for efficient usage of blood donation and decreasing the blood maintenance costs.

Author Biographies

  • Maryam Ashoori1, 1- Dept. of Information Technology Engineering, School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran.
    گروه مهندسی فناوری اطلاعات- مربی
  • Shahriar Mohammadi2, 2- Dept. of Information Technology Engineering, School of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
    گروه مهندسی فناوری اطلاعات- دانشکده مهندسی صنایع

References

Venkateswarlu B, Prasad Raju GSV. Mine Blood Donors Information through Improved K-Means Clustring. International Journal of Computational Science and Information Technology 2013;1:9-15.

Darwiche M, Feuilloy M, Bousaleh G, Schang D. Prediction of blood transfusion donation. In Fourth International Conference on Research Challenges in Information Science 2010;51-6. doi: 10.1109/RCIS.2010.5507363

Testik MC, Ozkaya BY, Aksu S, Ozcebe OI. Discovering blood donor arrival patterns using data mining: a method to investigate service quality at blood centers. Journal of Medical Systems 2012; 36:579-94. doi: 10.1007/s10916-010-9519-7

Saiful Islam AHM, Ahmed N, Hasan K, Jubayer M. mHealth: Blood Donation Service in Bangladesh. In International Conference on Informatics, Electronics & Vision 2013;1-6. doi: 10.1109/ICIEV.2013. 6572594

Li BN, Dong MC. Banking on blood [electronic donor card system]. Computing & Control Engineering Journal 2006;17:22-5.

Alfonso E, Xie X, Augusto V, Garraud O. Modeling and simulation of blood collection systems. Health Care Manag Sci 2012;15:63-78. doi: 10.1007/s10729-011-9181-8

Li BN, Dong MC, Chao S. On decision making support in blood bank information systems. Expert Systems with Applications 2008;34:1522-32. doi: 10.1016/j.eswa.2007.01.016

Shmiel O, Shmiel T, Dagan Y, Teicher M. Processing of Multichannel Recordings for Data-Mining Algorithms. IEEE Transactions on Biomedical Engineering 2007;54:444-53.

Altiparmak F, Ferhatosmanoglu H, Erdal S, Trost DC. Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases. IEEE Trans Inf Technol Biomed 2006;10: 254-63.

Seliya N, Khoshgoftaar TM. The use of decision trees for cost-sensitive classification: an empirical study in software quality prediction. WIREs Data Mining and Knowledge Discovery 2011; 1: 448-59. doi: 10.1002/widm.38

Loh WY. Classification and regression trees. WIREs Data Mining and Knowledge Discovery 2011;1:14-23. doi: 10.1002/widm.8

Chen X, Wang M, Zhang H. The use of classification trees for bioinformatics. Wiley Interdiscip Rev Data Min Knowl Discov 2011;1:55-63. doi: 10.1002/widm.14

Kokol P, Pohorec S, ˇStiglic G, Podgorelec V. Evolutionary design of decision trees for medical application. WIREs Data Mining and Knowledge Discovery 2012;2:237-54. doi: 10.1002/widm.1056

Santhanam T, Sundaram S. Application of CART algorithm in blood donors classification. Journal of Computer Science 2010;6: 548-52. doi : 10.3844/jcssp.2010.548.552

Lee WC, Cheng BW. An intelligent system for improving performance of blood donation. Journal of Quality 2011;18: 173-85.

Dhoke NW, Deshmukh SS. To improve blood donation process using data mining techniques. International Journal of Innovative Research in Computer and Communication Engineering 2015;3: 4834-40. doi: 10.15680/ijircce.2015.0305166

Alizadeh S, Ghazanfari M, Teimorpour B. Data mining and knowledge discovery. 2nd ed. Tehran: Publication of Iran University of Science and Technology;2011.[Persian].

Ameri H, Alizadeh S, Barzegari A. Knowledge extraction of diabetics’ data by decision tree method. Health Management 2013;16:58-72.[Persian].

López MI, Luna JM, Romero C, Ventura S. Classification via clustering for predicting final marks based on student participation in forum. Proceeding of 5th International Conference on Educational Data Mining; 2012 Jun 19-21; Greece,China.p.148-51.

Ashoori M, Taheri Z. Using clustering methods for identifying blood donors behavior. Proceeding of 5th Iranian Conference on Electrical and Electronics Engineering 2013; Gonabad, Iran.p.4055-77.

Han J, Kamber M. Data Mining: Concepts and Techniques. 2nd ed. Morgan Kaufman;2006.

Chen G, Asterbro T. How to deal with missing categorical data: test of a simple Bayesian Method. Organizational Research Methods 2003;6:309-27.

Papagiannis D, Rachiotis G, Symvoulakis EK, Anyfantakis D, Douvlataniotis K, Zilidis C, et al. Blood donation knowledge and attitudes among undergraduate health science students: A cross-sectional study. Transfus Apher Sci 2016;54:303-8. doi: 10.1016/j.transci.2015.11.001

Ramachandran P, Girija N, Bhuvaneswari T. Classifying blood donors using data mining techniques. IJCSET 2011;1:10-3.

Sundaram S, Santhanam T. Real-time blood donor management using dashboards based on data mining models. International Journal of Computer Science 2011;8:159-63.

Sundaram S, Santhanam T. A comparison of blood donor classification data mining models. Journal of Theoretical and Applied Information Technology 2011;30:98-101.

Ramoa A, Maia S, Lourenço A. A rational framework for production decision making in blood establishments. J Integr Bioinform 2012;9:1-11. doi: 10.2390/biecoll-jib-2012-204

Sharma A, Gupta PC. Predicting the number of blood donors through their age and blood group by using data mining tool. International Journal of Communication and Computer Technologies 2012;1:6-10.

Hari Ganesh S, Vanitha K. Comparative study of data mining approaches for blood platelet transfusion. International Journal of Advanced Research in Computer Engineering & Technology 2014; 3:3069-74.

Asha Rani S, Hari Ganesh S. A comparative study of classification algorithm on blood transfusion. International Journal of Advancements in Research & Technology 2014;3:57-60.

Ritika , Paul A. Prediction of blood donors‟ population using data mining classification technique. International Journal of Advanced Research in Computer Science and Software Engineering 2014;4:634-8.

Ashoori M, Alizade S, Hossieny H, Hossieny S. A model to predict the sequential behavior of healthy blood donors using data mining. Journal of Research & Health 2015; 5:141-8.

Published

2017-02-05

Issue

Section

Original Article(s)

How to Cite

Exploring Blood Donors’ Status Through Clustering: A Method to Improve the Quality of Services in Blood Transfusion Centers. (2017). Knowledge and Health in Basic Medical Sciences, 11(4), page:73-82. https://doi.org/10.22100/jkh.v11i4.1525

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