A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

Abstract

Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classication process by means of ELM was performed using the remaining attributes. The Wisconsin Breast Cancer dataset (WBCD), derived from the University of California Irvine machine learning database, was used for the purpose of testing the proposed hybrid model and the success rate of the RS + ELM model was determined as 100%. Moreover, the most appropriate attributes for the diagnosis of breast cancer were determined from the WBCD in this study. It is considered that the proposed method will be useful in similar medical practices.