Detection of Malignant and Benign Breast Cancer Using the ANOVA-BOOTSTRAP-SVM
Article Category: Research Paper
Published Online: May 20, 2020
Page range: 62 - 75
Received: Dec 22, 2019
Accepted: Apr 07, 2020
DOI: https://doi.org/10.2478/jdis-2020-0012
Keywords
© 2020 Borislava Petrova Vrigazova, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
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.