Identification of the recurrence of breast cancer by discriminant analysis

Abstract

Breast cancers enact one of the deadliest diseases that make a high number of deaths every year. It is a special type of all cancers and the primary reason for women’s deaths globally (Bangal et al. Breast, carcinoma in women—a rising threat [1]). In the medical field, data mining methods are widely used in diagnosis and analysis to make decisions exclusively. Given the relationship between the degree of malignancy and recurrence of breast cancer, and given the typical model of breast cancer spread, it should be the principal goal of early detection by which cancers can be identified when they are small and node-negative. Most of the researches in the field of breast cancer have focused on predicting and analyzing the disease. There is not much evidence in study of recurrence of the disease. This paper aims at developing an approach to predict and identify the probability of recurrence of breast cancer for the patients with greater accuracy.