Classification of Some Seasonal Diseases: A Hierarchical Clustering Approach

Samson Agboola; Mataimaki Benard Joel

doi:doi:10.11648/j.bsi.20170203.16

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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

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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), 2017. Published by Science Publishing Group

Keywords

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

References

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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

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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

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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

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@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}
}

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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  -

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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

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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

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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

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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

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@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