Journal of Data and Information Science ›› 2020, Vol. 5 ›› Issue (2): 62-75.doi: 10.2478/jdis-2020-0012

• Research Paper • Previous Articles     Next Articles

Detection of Malignant and Benign Breast Cancer Using the ANOVA-BOOTSTRAP-SVM

Borislava Petrova Vrigazova()   

  1. Department of Statistics and Econometric, Faculty of Economics and Business Administration, Sofia University, Bulgaria
  • Received:2019-12-22 Revised:2020-03-18 Accepted:2020-04-07 Online:2020-05-20 Published:2020-05-24
  • Contact: Borislava Petrova Vrigazova


Purpose: The aim of this research is to propose a modification of the ANOVA-SVM method that can increase accuracy when detecting benign and malignant breast cancer.

Methodology: We proposed a new method ANOVA-BOOTSTRAP-SVM. It involves applying the analysis of variance (ANOVA) to support vector machines (SVM) but we use the bootstrap instead of cross validation as a train/test splitting procedure. We have tuned the kernel and the C parameter and tested our algorithm on a set of breast cancer datasets.

Findings: By using the new method proposed, we succeeded in improving accuracy ranging from 4.5 percentage points to 8 percentage points depending on the dataset.

Research limitations: The algorithm is sensitive to the type of kernel and value of the optimization parameter C.

Practical implications: We believe that the ANOVA-BOOTSTRAP-SVM can be used not only to recognize the type of breast cancer but also for broader research in all types of cancer.

Originality/value: Our findings are important as the algorithm can detect various types of cancer with higher accuracy compared to standard versions of the Support Vector Machines.

Key words: Breast cancer detection, ANOVA, Bootstrap, Support vector machines