| Resumen |
Association rule mining plays a crucial role in the medical area in discovering interesting relationships
among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP
growth, or Eclat require considerable computation resources and generate large volumes of rules. Moreover,
these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit
some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective
algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records
with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing
classical and causal estimation measures on 4 waves defined as the periods of time where the number of
infected people increased. The proposed contributions generate, recombine and evaluate patterns, focusing
on recovering promising high-quality rules with actionable cause-effect relationships among attributes to
identify which groups are more susceptible to disease or what combinations of conditions are necessary to
receive certain types of medical care. |
