We are currently facing the global pandemic caused by COVID-19, in the case of Peru, this disease has caused the death of approximately 200,000 people (September 2021), being one of the countries with the most deaths per thousand people. Due to this, progress is being made in the vaccination process, of which it has been possible to immunize more than 72% of the population with two doses. However, according to data collected by the Peruvian government, the deaths of people who would have been inoculated with at least one dose have been recorded. The present work proposes to apply machine learning models (Machine Learning), where the factors that influence the death of people are analyzed despite having been vaccinated with at least one dose, to achieve this goal, unsupervised learning techniques such as Kmeans, Spectral Clustering, Gaussian Mixture, Hierarchical Clustering, as well as data visualization techniques were applied. The results obtained reveal that the main factors that led to death are elderly people, mostly men, and that their health centers are also far from their homes, in addition to not having had access to hospitalization for adequate treatment.