Machine Learning Models to Predict Recoveries and Deaths from COVID-19 in Mexican Society in the Post-Pandemic Era

The emergence or mutation of aggressive viruses represents a latent threat to human health that could lead to new pandemics, so it is important to constantly monitor and analyze the behavior of the diseases they can cause. In this sense, the purpose of this work was to generate models to predict the behavior of recoveries and deaths from COVID-19 in Mexico in the post-pandemic era, applying machine learning techniques to data related to this disease, published by the Mexican government. Models based on artificial neural networks, logistic regression, and classification algorithms were generated and validated, yielding high rates of correct classification, accuracy, and recall, so that they could be used to make predictions about future cases of patients infected with the SARS-CoV-2 virus.