RESEARCH ARTICLE Gene expression profiling a

Full list of author information is available at the end of the articleBackground Breast cancer is one of the most common cancers in women worldwide. According to the American Cancer Society, breast cancer is the second leading cause of death among women in the U.S.[1]. However, signifi-cantly different five-year recurrence rates and survival rates have been observed among breast cancer pa-tients with the same course of disease. In other words, prognostic factors for breast cancer recurrence, such as histology and lymph node status, cannot entirely correctly predict the subsequent clinical manifestations of patients [2,3]. Microarray technology can be used to acquire infor-mation about thousands of genes simultaneously. Traditional statistical methods, such as logistic regres-sion, have become increasingly difficult to use for sur-vivability prediction models due to several constraints that dictate the low statistical power with small sam-ple size and complex polynomial interaction terms with curvilinear effects among the relationship of vari-ables. Data mining techniques, such as artificial neura