CTCHABC- hybrid online sequential fuzzy Extreme Kernel learning method for detection of Breast Cancer with hierarchical Artificial Bee

Circulating Tumor Cells (CTCs) in Peripheral Blood (PB) testing is measured as significant investigative and promising microarray technology for breast cancer examination. Few numbers of the work have been proposed in earlier for the role of CTCs detection in breast cancer; but still the development of novel method for identification of CTC becomes difficult because of hundreds and thousands of indicative genes is presented. The main intention of the work is to the identification of CTC in PB during Breast Cancer (BC) regarding to Metastatic (MS), Non-Metastatic (NMS) and hybrid MS and NMS. The proposed method is not only the identification of CTC in BC, in addition it solves gene selection by proposing hybrid fuzzy online sequential Particle Swarm Genetic (PSG) kernel extreme learning machine finally named as (FOP-KELM) classification. The proposed FOP-KELM method calculates the mean values for each gene features and it is compared objective function of KELM to select and remove unimportant gene features. In order to reduce the fuzzy membership assumption value in ELM, it is optimized using PSG algorithm. The impact of selected features from FOP-KELM has been investigated using clustering method. To perform classification task for selected gene features, a novel Hierarchical Artificial Bee clustering algorithm (HABCA) is proposed. It capably distinguishes the CTC through the separation of tumor samples into a hierarchical tree structure in a top-down manner, where the distance between two gene tumor samples is determined by using ABC. Clustering results are classified into MS, NMS, MS and NMS.