Aplicación de algoritmos de clasificación como soporte al diagnóstico del cáncer de mama en unidades oncológicas

Abstract

Breast cancer is one of the diseases that cause a lot of deaths every year, it is the most common type of all cancers and one of the leading causes of death of women in the world and Colombia. In this article, we present the use of WEKA as a machine learning and data mining tool, applying different classifiers to a set of breast cancer data provided by the cancer unit with the objective of supporting the diagnosis and supporting the taking of decisions in the diagnosis of breast cancer. The results obtained show how researchers in the area of health can use both statistical analysis and data mining techniques to discover knowledge and make a better diagnosis of breast cancer and other diseases. In the document a comparison of the effectiveness of the classifiers and an analysis of correct classification, incorrect classification and precision are made, aspects that are of importance for researchers who want to use algorithms in other types of studies or with other attributes to diagnose if the values of these lead or not to conclude that a person has breast cancer