Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach

Samson Agboola; Mataimaki Benard Joel

doi:doi:10.11648/j.bsi.20170203.16

| Peer-Reviewed

Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach

Samson Agboola, Mataimaki Benard Joel

Published in Biomedical Statistics and Informatics (Volume 2, Issue 3)

Received: 22 July 2017 Accepted: 2 August 2017 Published: 4 September 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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.

Published in	Biomedical Statistics and Informatics (Volume 2, Issue 3)
DOI	10.11648/j.bsi.20170203.16
Page(s)	122-127
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Hierarchical, Clustering, Diseases, Classification, RMS-DBO, Techniques

References

[1]	Anderson, T. W. "Fisher and Multivariate Analysis." (Statistical Science journal) 11, no. 1, 20-34 (1996).
[2]	Blei, D. & Lafferty, J. (2009). Topic models. In A. Srivastava and M. Sahami (Eds.), Text Mining: Classification, Clustering, and Applications (pp. 71-94). Boca Raton, FL: Taylor & Francis Group.
[3]	Cornell, E. John, et al. "Multimorbidity Clusters: Clustering binary data from multimorbidity cluster: Clustering binary data from a large administrative database." Applied Multivariate Research, 2007: 163-182.
[4]	Dauda, U, S. U Gulumbe, M Yakubu, and L. K Ibrahim. "Monetering of Infectious Diseases in Katsina and Daura Zones of Katsina State: A Clustering Analysis." Nigerian Journal of Basic and Applied Science, 2011: 31-42.
[5]	Everitt, B. S. "Cluster Analysis", Heinemann Educational Book Ltd, UK. 1974.
[6]	Fraley C. and Raftery A. E., “How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis”, Technical Report No. 329. Department of Statistics University of Washington, 1998.
[7]	Hands, S. and Everitt, B. (1987). A Monte Carlo study of the recovery of cluster structure in binary data by hierarchical clustering techniques. Multivariate Behavioral Research, 22, 235–243.
[8]	Norusis, M. J. (2010). Chapter 16: Cluster analysis. PASW Statistics 18 Statistical Procedures Companion (pp. 361-391). Upper Saddle River, NJ: Prentice Hall.
[9]	Nwabueze, Joy Chioma. "Statistical grouping of cassava mosaic disease-resistant varieties cultivated by the National Root Crops Research Institute, Umudike, Nigeria." African Journal of Mathematics and Computer Science Research, 2013: 26-34.
[10]	Han, J. and Kamber, M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
[11]	Hartigan, J. A. "Direct Clustering of Data Matrix." (Journal of American statistical Association) 67, no. 123-129 (1972).
[12]	Gulumbe, S. U., Bakar, A. B. and Dikko, H. G. (2008). Classification of some HIV/AIDS Variables, a multivariate approach. Res. J. Sci. 15: 24 – 30.
[13]	Jain, Milan, and Setu Kumar Chaturvedi. "Quantum Computing Based Technique for Cancer Disease Detection System." Journal of Computer Science & Systems Biology, 2014: 9.
[14]	Johnson, R. A, and D. W Wichern. "Applied Multivariate Statistical Analysis." Prince Hall, 2002.
[15]	Morissette, L., & Chartier, S. (2013). The k-means clustering technique: General considerations and implementation in Mathematical. Tutorials in Quantitative Methods for Psychology, 9 (1), 15-24.
[16]	Morrison, D F. "Multivariate Statistical Method." (MC Craw Hill) 1990.
[17]	Nwabueze, Joy Chioma. "Statistical grouping of cassava mosaic disease-resistant varieties cultivated by the National Root Crops Research Institute, Umudike, Nigeria." African Journal of Mathematics and Computer Science Research, 2013: 26-34.
[18]	Ryan, J. V. "Classification and Clustering." (Academic Press Inc) 1977.
[19]	Sneath, P., and Sokal, R. Numerical Taxonomy. W. H. Freeman Co., San Francisco, CA, 1973.
[20]	Tarpey, T. (2007). Linear transformations and the k-means clustering algorithm. The American Statistician, 61, 34–40.
[21]	Wilmink, F. W. & Uytterschaut, H. T. (1984). Cluster analysis, history, theory and applications. In G. N. van Vark & W. W. Howells (Eds.), Multivariate Statistical Methods in Physical Anthropology (pp. 135-175). Dordrecht, The Netherlands: D. Reidel Publishing Company.

Cite This Article

Plain Text BibTeX RIS

APA Style

Samson Agboola, Mataimaki Benard Joel. (2017). Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach. Biomedical Statistics and Informatics, 2(3), 122-127. https://doi.org/10.11648/j.bsi.20170203.16

Copy | Download

ACS Style

Samson Agboola; Mataimaki Benard Joel. Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach. Biomed. Stat. Inform. 2017, 2(3), 122-127. doi: 10.11648/j.bsi.20170203.16

Copy | Download

AMA Style

Samson Agboola, Mataimaki Benard Joel. Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach. Biomed Stat Inform. 2017;2(3):122-127. doi: 10.11648/j.bsi.20170203.16

Copy | Download

@article{10.11648/j.bsi.20170203.16,
  author = {Samson Agboola and Mataimaki Benard Joel},
  title = {Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {3},
  pages = {122-127},
  doi = {10.11648/j.bsi.20170203.16},
  url = {https://doi.org/10.11648/j.bsi.20170203.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.16},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach
AU  - Samson Agboola
AU  - Mataimaki Benard Joel
Y1  - 2017/09/04
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170203.16
DO  - 10.11648/j.bsi.20170203.16
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 122
EP  - 127
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170203.16
AB  - 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.
VL  - 2
IS  - 3
ER  -

Copy | Download

Author Information

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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Samson Agboola, Mataimaki Benard Joel. (2017). Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach. Biomedical Statistics and Informatics, 2(3), 122-127. https://doi.org/10.11648/j.bsi.20170203.16

Copy | Download

ACS Style

Samson Agboola; Mataimaki Benard Joel. Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach. Biomed. Stat. Inform. 2017, 2(3), 122-127. doi: 10.11648/j.bsi.20170203.16

Copy | Download

AMA Style

Samson Agboola, Mataimaki Benard Joel. Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach. Biomed Stat Inform. 2017;2(3):122-127. doi: 10.11648/j.bsi.20170203.16

Copy | Download

@article{10.11648/j.bsi.20170203.16,
  author = {Samson Agboola and Mataimaki Benard Joel},
  title = {Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach},
  journal = {Biomedical Statistics and Informatics},
  volume = {2},
  number = {3},
  pages = {122-127},
  doi = {10.11648/j.bsi.20170203.16},
  url = {https://doi.org/10.11648/j.bsi.20170203.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20170203.16},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach
AU  - Samson Agboola
AU  - Mataimaki Benard Joel
Y1  - 2017/09/04
PY  - 2017
N1  - https://doi.org/10.11648/j.bsi.20170203.16
DO  - 10.11648/j.bsi.20170203.16
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 122
EP  - 127
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20170203.16
AB  - 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.
VL  - 2
IS  - 3
ER  -

Copy | Download