ClusteringProposal Supportfor theCOVID-19 Making Decision Process in a Data Demanding Scenario
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Orjuela Canon, Alvaro David | 2021
The COVID-19 disease surprised the world in the last monthsdue to the number of infections and deaths have been increased in an exponential way.Since the pandemic was established by the World Health Organization, different strategies have been proposedfordealingdiverse problems in cities that the coronavirus affected. This work presents a method to decision making support processes, specificallyin environment with few data and variables to be considered. Thus, artificial neural networks architectures were employed to cluster the informationavailable intheBogota city, and provide a tool that allows generatingadditional findings in a simultaneous mode, andexpressed as a visualmap. The present proposal reachedsensitivity measures around 75%, obtaining100% for thebest cases.
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