Breast cancer is one of the leading cancers for women when compared to all other cancers. It is the second most common cause of cancer death in women. The early detection and treatment of breast cancer can greatly improve the survival rate of patients. In this paper, the aim is to improve predictive performance by presenting a new hybrid classifier through the combination of support vector machine SVM and the grey wolf optimization GWO. We analyze the breast Cancer data available from the Wisconsin dataset from UCI machine learning with the aim of developing accurate prediction models for breast cancer using these hybrid methods. The breast cancer data with a total 569 rows and 32 columns will be used to train and test, by using hybrid methods namely SVM and GWO and achieve accuracy better than using SVM only with the same data. The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against SVM. |
