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This work is part of the statistical learning of data for model development. More specifically, it is devoted to the study of random forests which are one of the most recent algorithms in the family of pattern classification methods. A great advantage of random forest is that it can be used for both classification and regression problems, which constitute the majority of current machine learning systems. In this work, we used coronavirus data to generate and evaluate the proposed model. We chose random forests, which apply uniform majority voting of decision trees to produce an optimal prediction, to classify patients who tested positive or negative for coronavirus. The data were used as a training sample to design a decision model.