Volume 2, Issue 3, September 2017, Page: 122-127
Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach
Samson Agboola, Department of Statistics, Faculty of Physical Science, Ahmadu Bello University, Zaria, Nigeria
Mataimaki Benard Joel, Department of Statistics, Faculty of Physical Science, Ahmadu Bello University, Zaria, Nigeria
Received: Jul. 22, 2017;       Accepted: Aug. 2, 2017;       Published: Sep. 4, 2017
DOI: 10.11648/j.bsi.20170203.16      View  1113      Downloads  80
Abstract
This study compared six (6) agglomerative hierarchical clustering techniques namely Single-linkage, Complete-linkage, Centroid hierarchical, group average linkage, median hierarchical and ward’s minimum variance on some seasonal diseases to know which technique is most appropriate for classification. These seasonal diseases where gotten from five (5) different hospitals namely; Jamaa, Salama, Almadina, Gambo Sawaba and St Lukes Hospitals in Zaria. The Root Mean Square Distance Between Observation (RMS-DBO) which gives the best technique (s) for classification showed that the single-linkage and complete-linkage was the best techniques for the classification of the diseases. The results were calculated using R and SAS packages. The study achieves the best clustering technique for the classification of the studied seasonal diseases.
Keywords
Hierarchical, Clustering, Diseases, Classification, RMS-DBO, Techniques
To cite this article
Samson Agboola, Mataimaki Benard Joel, Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach, Biomedical Statistics and Informatics. Vol. 2, No. 3, 2017, pp. 122-127. doi: 10.11648/j.bsi.20170203.16
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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