Abstract
The State of Guanajuato, located in the center of Mexico, is one of the regions of the country with a high rate of infections of the SARS-CoV-2 virus in relation to its population size, according to official data provided by the federal government. Motivated by this fact, we undertook to further analyze such data in order to identify correlations between a possible complication of the COVID-19 disease, caused by the SARS-CoV-2 virus, and some non-transmissible chronic diseases and other comorbidities. To carry out our study, we rely on the KDD methodology and specialized machine-learning tools, that allow to extract hidden knowledge in the data, which cannot usually be obtained using traditional information analysis techniques. In this way, initially, the cases infected by the SARS-CoV-2 virus were characterized in a general way and, later, classification models were built to identify some rules among the comorbidity variables.
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