Version 1
: Received: 10 June 2023 / Approved: 12 June 2023 / Online: 12 June 2023 (05:10:55 CEST)
How to cite:
Ahmed, M.K.; Sharma, D.P.; Worku, H.S.; Tahir, A.I. Livestock Disease Data Management for E-Surveillance and
Disease Mapping Using Cluster Analysis. Preprints2023, 2023060767. https://doi.org/10.20944/preprints202306.0767.v1
Ahmed, M.K.; Sharma, D.P.; Worku, H.S.; Tahir, A.I. Livestock Disease Data Management for E-Surveillance and
Disease Mapping Using Cluster Analysis. Preprints 2023, 2023060767. https://doi.org/10.20944/preprints202306.0767.v1
Ahmed, M.K.; Sharma, D.P.; Worku, H.S.; Tahir, A.I. Livestock Disease Data Management for E-Surveillance and
Disease Mapping Using Cluster Analysis. Preprints2023, 2023060767. https://doi.org/10.20944/preprints202306.0767.v1
APA Style
Ahmed, M.K., Sharma, D.P., Worku, H.S., & Tahir, A.I. (2023). Livestock Disease Data Management for E-Surveillance and
Disease Mapping Using Cluster Analysis. Preprints. https://doi.org/10.20944/preprints202306.0767.v1
Chicago/Turabian Style
Ahmed, M.K., Hussein Seid Worku and Amir Ibrahim Tahir. 2023 "Livestock Disease Data Management for E-Surveillance and
Disease Mapping Using Cluster Analysis" Preprints. https://doi.org/10.20944/preprints202306.0767.v1
Abstract
This study investigates how Electronic Livestock Health Recording Systems (ELHRs)
facilitates the detection of disease burden and make cluster analysis by applying data
analytics tools and techniques. A sample size of 18333 livestock disease cases reported from
2007-2015 by the Ministry of Agriculture of the Federal Democratic of Ethiopia was used for
data collection. The results showed that ELHRs are important as livestock disease data
preservers, saving costs, and facilitating the extraction of up-to-date and complete
information. Euclidean and Manhattan distance performed well at 98%, while cosine distance
measurement metrics performed poorly. Finally, with the application of the selected
clustering techniques, metrics, tools, and dataset, it has been attempted to successfully detect
an optimal number of disease clusters and meet the objectives of the study.
Keywords
Data analytics; Cluster analysis; Disease mapping; Distance metrics; livestock Disease
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
