Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Livestock Disease Data Management for E-Surveillance and Disease Mapping Using Cluster Analysis

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. 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. Preprints 2023, 2023060767. 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

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