An intelligent system for improving performance of blood donation

Journal of Quality

Volumn 18, Issue 2, 2011, Pages 173-185

  Lee, Wen Chen ^a   Cheng, Bor Wen ^a  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Blood donation; Clustering and classification; Donation intention

Indexed keywords

BLOOD DONATION; BLOOD SUPPLY; CLASSIFICATION ALGORITHM; CLINICAL MEDICINE; CLUSTERING ANALYSIS; CLUSTERING AND CLASSIFICATION; DATA SETS; DONATION INTENTION; FOUR-GROUP; IMPROVING PERFORMANCE; SERVICE QUALITY;

BEHAVIORAL RESEARCH; CLUSTERING ALGORITHMS; INTELLIGENT SYSTEMS; MEDICINE;

BLOOD;

EID: 79955733523     PISSN: 10220690     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Ajzen, I., 1991, The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50(2), 179-211.
2. Banerjee, A. and Dave, R. N., 2004, Validating clusters using the Hopkins statistic, The Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004), 149-153.
3. Beal, R. W. and van Aken, W. G., 1992, Gift or good? a contemporary examination of the voluntary and commercial aspects of blood donation, Vox Sanguinis, 63(1), 1-5.
4. Borgelt, C. and Kruse, R., 2002, Graphical models-methods for data analysis and mining, John Wiley and Sons, New York.
5. Breiman, L., 1996, Bagging predictors, Machine Learning, 24(2), 123-140.
6. Cooper, M. H. and Culyer, A. J., 1968, The price of blood: an economic study of the charitable and commercial principle, Institute of Economic Affairs, London.
7. Eder, A., Goldman, M., Rossmann, S., Waxman, D. and Bianco, C., 2009, Selection criteria to protect the blood donor in north america and Europe: past (dogma), present (evidence), and future (hemovigilance), Transfusion Medicine Reviews, 23(3), 205-220.
8. Ferguson, E., 1996, Predictors of future behavior: a review of the psychological literature on blood donation, British Journal of Health Psychology, 1(4), 287-308.
9. France, J. L., France, C. R. and Himawan, L. K., 2007, A path analysis of intention to redonate among experienced blood donors: an extension of the theory of planned behavior, Transfusion, 47(6), 1006-1013.
10. Gloria, P. W., Sharkey, P. and Dy, S. M., 2008, Mining lung cancer patient data to assess healthcare resource utilization, Expert Systems with Applications, 35(4), 1611-1619.
11. Kim, Y. S., 2009, Boosting and measuring the performance of ensembles for a successful database marketing, Expert Systems with Applications, 36(2), 2161-2176.
12. Kohavi, R., 1996, Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid, The Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 202-207.
13. Kuncheva, L. I., 2004, Combining pattern classifiers: Methods and algorithms, Wiley, New York.
14. Lemmens, K. P. H., Abraham, C., Hoekstra, T., Ruiter, R. A. C., De Kort, W. L. A. M., Brug, J. and Schaalma, H. P., 2005, Why don't young people volunteer to give blood? an investigation of the correlates of donation intentions among young nondonors, Transfusion, 45(6), 945-955.
15. Linden, J. V., Gregorio, D. I. and Kalish, R. I., 1988, An estimate of blood donor eligibility in the general population, Vox Sanguinis, 54(2), 96-100.
16. MacQueen, J. B., 1967, Some methods for classification and analysis of multivariate observations, The Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 281-297.
17. Mitchell, T., 1997, Machine learning, McGraw-Hill, New York.
18. Moog, R. and Fourne, K., 2007, Recruitment of prospective donors: what do they expect from a homepage of a blood transfusion service? Transfusion Medicine, 17(4), 279-284.
19. Ownby, H. E., Kong, F., Watanabe, K., Tu, Y. and Nass, C. C., 1999, Analysis of donor return behavior. Retrovirus epidemiology donor study, Transfusion, 39(10), 1128-1135.
20. Ramsey, G. and Schmidt, P. J., 2009, Transfusion medicine in Chicago, before and after the "blood bank", Transfusion Medicine Reviews, 23(4), 310-321.
21. Ringwald, J., Lange, N., Rabe, C., Zimmermann, R., Strasser, E., Hendelmeier, M., Strobel, J. and Eckstein, R., 2007, Why do some apheresis donors donate blood just once? Vox Sanguinis, 93(4), 354-362.
22. Rodriguez, J. J., Kuncheva, L. I. and Alonso, C. J., 2006, Rotation forest: a new classifier ensemble method, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1619-1630.
23. Smyth, P., Gray, A. and Fayyad, U., 1995, Retrofitting decision tree classifiers using kernel density estimation, The Proceedings of the 12th International Conference on Machine Learning, 506-514.
24. Taiwan blood services foundation, (accessed March, 2010), Annual statistics report.
25. Thomson, R. A., Bethel, J., Lo, A. Y., Ownby, H. E., Nass, C. C. and Williams, A. E., 1998, Retention of "safe" blood donors. The retrovirus epidemiology donor study, Transfusion, 38(4), 359-367.
26. Titmuss, R., 1970, The gift relationship: from human blood to social policy, Allen and Unwin, London.
27. Wu, Y., Glynn, S. A., Schreiber, G. B., Wright, D. J., Lo, A., Murphy, E. L., Kleinman, S. H. and Garratty, G., 2001, First-time blood donors: demographic trends, Transfusion, 41(3), 360-364.
28. Yeh, I. C., Yang, K. J. and Ting, T. M., 2009, Knowledge discovery on RFM model using Bernoulli sequence, Expert Systems with Applications, 36(3), 5866-5871.