Generation of synthetic data to evaluate the bovine leukosis infectious disease
DOI:
https://doi.org/10.24054/rcta.v1i41.2556Keywords:
Machine learning, Bovine infectious diseases, Synthetic data, LeucosisAbstract
The projects that are conducted in the animal health sector face technological and scientific limitations due both to the lack of consistent and reliable information, and to the high costs of collecting information for farmers. Likewise, legal limitations on the disclosure of information for reasons such as data protection laws lead to delays in the development of policies and strategies, as well as in decision-making. Given this lack of information availability, the generation of synthetic data from a set of original data emerges as a solution. Thus, this paper presents a study through which three methods to generate synthetic data that reflect the behavior of a bovine disease in a set of real data were evaluated. The work was based on comparing machine learning algorithms, tools, and model-based methods to improve the realism of synthetic data of disease behavior. The goal was to find the best model for the generation of synthetic data using the case of the bovine mastitis infectious disease, since there is not enough data for it. In order to validate the synthetic data, it was necessary to contrast the original data set and the synthetic information, looking for the selected method to generate synthetic data with qualities similar to those of the original data set.
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