Breast Cancer Detection Based on Feature Selection Using Enhanced Grey Wolf Optimizer and Support Vector Machine Algorithms

Abstract

Breast cancer is the leading cause of high fatality among women population. Identification of the benign and malignant tumor at correct time plays a critical role in the diagnosis of breast cancer. In this paper, an attempt has been made to extract the valuable information by selecting the relevant features using our proposed EGWO-SVM (enhanced grey wolf optimization-support vector machine) approach. Grey wolf optimizer (GWO) has gained a lot of popularity among other swarm intelligence methods due to its various characteristics like few tuning parameters, simplicity and easy to use, scalable, and most importantly its ability to provide faster convergence by maintaining the right balance between the exploration and exploitation during the search. Therefore, an enhanced GWO has been proposed in combination with SVM to determine the optimum subset of tumor features for accurate identification of benign and malignant tumor. The proposed approach has been tested and compared with numerous existing, state-of-the-art as well as recently published breast cancer classification approaches on the standard benchmark Wisconsin Diagnostic Breast Cancer (WDBC) database. The proposed approach outperforms all the compared approaches by improving the classification accuracy to 98.24% demonstrating its effectiveness in identifying the breast cancer.