A Comparative Study of Meta Heuristic Model to Assess the Type of Breast Cancer Disease

Abstract

Breast cancer is the expansion of cancer from the breast tissue and a lump in the breast, a change in breast shape, dimpling of the skin, fluid coming from the nipple or a red scaly patch of skin. This disease is becoming a leading cause of death among women in the whole world; meanwhile, if it is recognized in the early stage and accurate diagnosis of this disease can ensure a long survival of women. Here, the knowledge base has been created using the concept of data mining to recognize the disease based on the early data. In this paper, data mining has been used to create the association rules and statistical, soft computing and evolution algorithms have been used to select optimal classifier based on earlier data. In this context, three types (training on the whole dataset and new tested data have been created, tenfold and 98% training and 2%tested data) of techniques have been used to estimate the disease type (named consequent item) based on the nine attributes (named antecedent item) of the breast cancer data set.