Early diagnosis of breast cancer by gene expression profiles

Abstract

Breast cancer is the second leading cause of cancer death in women worldwide. Nevertheless, there is evidence that early detection and treatment can increase the survival rate of breast cancer patients. This paper presents an intelligent decision support system (IDSS) for breast cancer diagnosis by using gene expression profiles. The proposed system first extracts significant features from the input patterns by using information gain and then employs deep genetic algorithm for feature reduction as well as for breast cancer diagnosis. The proposed system is evaluated by considering a benchmark microarray dataset and compared with the most recent systems. The results show that the proposed IDSS outperforms other systems in terms of diagnosis time and accuracy. The proposed system produces 100 % classification accuracy. In addition, the proposed system reduces the required memory space.