Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach

Abstract

Breast cancer is the most prevalent type of cancer found in women worldwide. It is becoming a leading cause of death among women in the whole world. Early detection and effective treatment of this disease is the only rescue to reduce breast cancer mortality. Because of the effective classification and high diagnostic capability expert systems are gaining popularity in this field. But the problem with machine learning algorithms is that if redundant and irrelevant features are available in the dataset then they are not being able to achieve desired performance. Therefore, in this paper, a simultaneous feature selection and classification technique using Genetic Programming (GPsfsc) is proposed for breast cancer diagnosis. To demonstrate our results, we had taken the Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) databases from UCI Machine Learning repository and compared the classification accuracy, sensitivity, specificity, confusion matrix, and Mann Whitney test results of GONN with classical multi-tree GP algorithm for feature selection (GPmtfs). The experimental results on WBC and WDBC datasets show that the proposed method produces better classification accuracy with reduced features. Therefore, our proposed method is of great significance and can serve as first-rate clinical tool for the detection of breast cancer.