A novel machine learning approach for breast cancer diagnosis.

• Early prediction for mammography. • To diagnose the breast cancer at the earliest to reduce the mortality rate. • This paper proposes an expert system for the diagnosis of breast cancer disease using DE-RBF-KELM method. • The proposed method is effectively classifying the abnormal classes of mammograms. Breast cancer disease is a major public health problem among women worldwide. This article proposes an expert system for the diagnosis of breast cancer disease based on an evolutionary algorithm known as Differential Evolution (DE) of a Radial-Based Function Kernel Extreme Learning Machines (RBF-KELM). In the structure of the RBF-KELM, there are two adjustable parameters of the RBF-kernel which are the penalty parameter C and the RBF-kernel's parameter (σ). These parameters play a major role in the efficiency of RBF-KELM. In this study, the optimal values of these parameters have been obtained using a differential evolution (DE) algorithm. To validate the effectiveness of the suggested approach, DE-RBF-KELM was examined on the two datasets: The Mammographic Image Analysis Society (MIAS) and the wisconsin breast cancer database (WBCD) and the results were satisfactory compared to conventional approaches. [ABSTRACT FROM AUTHOR]