Volume 21, Issue 71 (4-2018)
jha 2018, 21(71): 66-78 |
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Rajabi J, Alizade S, Ashoori M. Identifying behavioral patterns in blood donation using K-means algorithm based on recency, frequency and blood value. jha 2018; 21 (71) :66-78
URL: http://jha.iums.ac.ir/article-1-2454-en.html
URL: http://jha.iums.ac.ir/article-1-2454-en.html
1- School of Industrial Engineering, K.N.Tossi University of Technology, Tehran, Iran
2- School of Industrial Engineering, K.N.Tossi University of Technology, Tehran, Iran.
3- School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran ,mashoori@Saravan.ac.ir
2- School of Industrial Engineering, K.N.Tossi University of Technology, Tehran, Iran.
3- School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran ,
Abstract: (3993 Views)
Introduction: Blood donation rate in developed countries is 18 times higher than developing countries. It is estimated that if only five percent of Iran population embark on blood donation, it will be adequate to meet the needs of the community. The aim of this paper is to identify the blood donators’ loyalty behavior for proper planning to extend and enhance blood donation habits among the community.
Methods: A cross-sectional survey was applied through census in the present study. The data extracted from blood transfusion center of Tehran from 2005 to 2010 was used in this study. Clementine 12.0 was used for data analysis. K-Means clustering is based on demographic data and RFM values modes which were applied to obtain the best ratio among different fields.
Results: The mean value of root mean square standard deviation for RFB-based clustering and demographic were 10.7194 and 11.1411,respectively. This finding confirmed better data clustering by RFB-based algorithm. The data were placed by RFB-based algorithm in four clusters. The fourth cluster consisted of single males who obtained the best loyalty rank and the third cluster consisted of married females who obtained the least loyalty rank.
Conclusion: Applying data mining methods for analysis and classification of blood donors changes current attitude towards blood donation procedure. Survey of donor behavior helps us to identify faster and more precise donor loyalty as well as having proper management of the blood transfusion centers.
Methods: A cross-sectional survey was applied through census in the present study. The data extracted from blood transfusion center of Tehran from 2005 to 2010 was used in this study. Clementine 12.0 was used for data analysis. K-Means clustering is based on demographic data and RFM values modes which were applied to obtain the best ratio among different fields.
Results: The mean value of root mean square standard deviation for RFB-based clustering and demographic were 10.7194 and 11.1411,respectively. This finding confirmed better data clustering by RFB-based algorithm. The data were placed by RFB-based algorithm in four clusters. The fourth cluster consisted of single males who obtained the best loyalty rank and the third cluster consisted of married females who obtained the least loyalty rank.
Conclusion: Applying data mining methods for analysis and classification of blood donors changes current attitude towards blood donation procedure. Survey of donor behavior helps us to identify faster and more precise donor loyalty as well as having proper management of the blood transfusion centers.
Type of Study: Research |
Received: 2017/06/26 | Accepted: 2018/01/10 | Published: 2018/03/17
Received: 2017/06/26 | Accepted: 2018/01/10 | Published: 2018/03/17
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