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Machine Learning Clustering Method for Analysis of Blood Donor Deferral

by Shashikala B.M., Pushpalatha M.P., Vijaya B.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 27
Year of Publication: 2021
Authors: Shashikala B.M., Pushpalatha M.P., Vijaya B.
10.5120/ijca2021921659

Shashikala B.M., Pushpalatha M.P., Vijaya B. . Machine Learning Clustering Method for Analysis of Blood Donor Deferral. International Journal of Computer Applications. 183, 27 ( Sep 2021), 40-43. DOI=10.5120/ijca2021921659

@article{ 10.5120/ijca2021921659,
author = { Shashikala B.M., Pushpalatha M.P., Vijaya B. },
title = { Machine Learning Clustering Method for Analysis of Blood Donor Deferral },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2021 },
volume = { 183 },
number = { 27 },
month = { Sep },
year = { 2021 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number27/32101-2021921659/ },
doi = { 10.5120/ijca2021921659 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:18:05.266170+05:30
%A Shashikala B.M.
%A Pushpalatha M.P.
%A Vijaya B.
%T Machine Learning Clustering Method for Analysis of Blood Donor Deferral
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 27
%P 40-43
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this paper is to classify the deferred donors based on the risk factors. This paper discusses the implementation of clustering technique with related to risk factors associated with the donors for becoming deferred donors. The data for this implementation is collected from local hospitals. The developed system is an unsupervised learning technique. The K- means clustering analysis work is utilized to arrange the blood contributor’s depending on the deferral reason. Elbow method used to identify the optimal number of clusters.

References
  1. Rahman, M.S., Akter, K.A.,Hossain,S.,Basak,A., and Ahmed, S.I.2011.” Smart blood query: a novel mobile phone based privacy aware blood donor recruitment and management system for developing regions”, IEEE Workshops of International Conference.
  2. Naji Moghadam, V., Ashoori, M., Alizadeh, S., and Safi, M. 2012.” The Classification Algorithm for number of tablet usage prediction: case study diabetes”, The sixth Iran Data Mining Conference, Tehran: Iran Data Mining.
  3. Rashid Mehrabadi, E. and Pedram, M.M. 2010.” Blood Donors Classification and Identifying Future Donors”, The Fourth Iran Data Mining Conference, Sharif University of Technology, Tehran, Iran.
  4. Sobia Zahra, Mustansar Ali Ghazanfar, Asra Khalid, Muhammad Awais Azam, Usman Naeem b, and Adam Prugel-Bennett.2015. “Novel Centroid Selection Approaches for Means -Clustering Based Recommender Systems”, Elsevier.
  5. Rajput, A., R.P. Chandel, N., Solanki,D.D., and Soni, R..2009.” Approaches of classification to policy of analysis of medical data”, International Journal of computer science and network security.
  6. K Selvamani. And Ashok Kumar Pai.2015. “A novel technique for online blood bank management”, International Conference on Intelligent Computing, Communication & Convergence, Elsevier.
  7. Bhardwaj, A., Sharma, A., and Shrivastava, V.K.2012.” Data Mining Techniquesand Their Implementation in Blood Bank Sector –A Review”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, no.4, pp. 1303-1309.
  8. Shashikala, B M., Puspalatha,M P., and Vijaya,B.2017.” Web Based Blood Donation Management System (BDMS) and Notifications “, International conference on Cognitive Computing and Information Processing, CCIP (Springer).
  9. Chen Lee,W. and Cheng, B. W .2011.” An intelligent system for improving performance of blood donation”, Journal of Quality, vol.18, no.2, pp.173-178.
  10. Ramachandran, P., Girija, N., and Bhuvaneswari, T. 2011.” Classifying BloodDonors Using Data Mining Techniques”, International Journal of Computer Science & Engineering Technology (IJCSET), vol. 1, no.1, pp.10-13.
  11. Mohamed, M. Mostafa .2009.” Profiling blood donors in Egypt: A neural networkAnalysis”, Expert Systems with Applications an International Journal, vol. 36, pp. 5031-5038.
  12. Bondu Venkateswarlu, G. S., V Prasda Raju.2013.” Mine Blood Donors Information through Improved K-Means Clustering”, International Journal of Computational Science and Information Technology.
  13. Vijayarani, S., and Sudha, S.2015. “An Efficient Clustering Algorithm for Predicting Diseases from Hemogram Blood Test Samples”, Indian Journal of Science and Technology.
  14. Sheshasaayee A., and Sharmila, P.2014.” Comparative study of fuzzy C means and K means Algorithm for requirements clustering”, Indian Journal of Science and Technology, vol.7, no.6, pp.853-857
  15. Maryam Ashoori, and Zahra Taheri.2013 “Using Clustering Methods for Identifying Blood Donors Behavior”,5th Iranian conference on electrical and electronics engineering, IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Deferred donors Risk factors Clustering technique Elbow method

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