Unbalanced breast cancer data classification using novel fitness functions in genetic programming

Abstract

"Breast Cancer is a common disease and to prevent it, the disease must be identified at earlier stages. Available breast cancer datasets are unbalanced in nature, i.e. there are more instances of benign (non-cancerous) cases then malignant (cancerous) ones. Therefore, it is a challenging task for most machine learning (ML) models to classify between benign and malignant cases properly, even though they have high accuracy. Accuracy is not a good metric to assess the results of ML models on breast cancer dataset because of biased results. To address this issue, we use Genetic Programming (GP) and propose two fitness functions. First one is F2 score which focuses on learning more about the minority class, which contains more relevant information, the second one is a novel fitness function known as Distance score (D score) which learns about both the classes by giving them equal importance and being unbiased. The GP framework in which we implemented D score is named as D-score GP (DGP) and the framework implemented with F2 score is named as F2GP. The proposed F2GP achieved a maximum accuracy of 99.63%, 99.51% and 100% for 60-40, 70-30 partition schemes and 10 fold cross validation scheme respectively and DGP achieves a maximum accuracy of 99.63%, 98.5% and 100% in 60-40, 70-30 partition schemes and 10 fold cross validation scheme respectively. The proposed models also achieves a recall of 100% for all the test cases. This shows that using a new fitness function for unbalanced data classification improves the performance of a classifier. "