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.