Análisis de expedientes clínicos que apoyan al diagnóstico de cáncer a partir de memorias asociativas evolutivas

Abstract

Cancer disease in all its types is still being studied in order to better understand it, given that its condition is of various forms and there are many factors that can be related to a final diagnosis of whether a person has a certain type of cancer. In this work, we present a first approach for the analysis of breast cancer, based on a well-recognized database of clinical records in the pattern recognition study community. The use of evolutionary associative memories is proposed as an analysis tool from machine learning, which according to the research carried out in the state of the art has not been used so far, and these have shown promising results. Our goal is to provide a new point of view of cancer factors as components of patterns; and analyzing the classification behavior from a known database. It should be noted that it is not intended to reduce the size of the pattern, but rather to shed light on the factors possibly related to the disease.