Exploring breast cancer prediction for cuban women

Abstract

The importance of early detection of breast cancer in the healthcare field has led to the generation of various models for estimating the risk of suffering from it. This paper analyzes the effectiveness of the official model used in the United States for this purpose, known as the Gail model, on a set of cases of Cuban native women. Despite the fact that the version of the model used considers the estimation of risk for Hispanic women born in the United States, certain limitations were found in the results, so the use of computational models based on machine learning applied to the same set of cases is explored as an alternative. The results show that, for the analyzed cases, better results are obtained using some machine learning algorithms, which gives rise to a greater exploration of these as an alternative to traditional models.