Extreme learning machine based approach for diagnosis and analysis of breast cancer

Due to the heterogeneous and complex nature of clinical data, the need to use sophisticated diagnosis techniques has increased significantly in recent years. The proposed approach for diagnosis of breast cancer exploits the potential of an extreme learning machine (ELM) and analyzes its performance after classification into benign and malignant cases. To optimize the ELM network in terms of computation time and memory resources, weight pruning is used without performance compromise. Using real data sets, numerical experiments have been conducted. With an accuracy of 93%, the optimum numbers of node layers for breast cancer diagnosis has been found to be 20. Comparative results demonstrate over-performance of the proposed ELM approach.