Decision Support System for the Breast Cancer Diagnosis by a Meta-learning Approach based on Grammar Evolution

Abstract

The incidence of breast cancer varies greatly among countries, but statistics show that every year 720,000 new cases will be diagnosed world-wide. However, a low percentage of women who suffer it can be detected using mammography methods. Therefore, it is necessary to develop new strategies to detect its formation in early stages. Many machine learning techniques have been applied in order to help doctors in the diagnosis decision process, but its definition and application are complex, getting results which are not often the desired. In this article we present an automatic way to build decision support systems by means of the combination of several machine learning techniques using a Meta-learning approach based on Grammar Evolution (MGE). We will study its application over different mammographic datasets to assess the improvement of the results.