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

• 作者: Borislava Petrova Vrigazova
• 来源期刊: 数据与情报科学学报:英文版
• 学科: 工学
• 关键词: Breast cancer detection ANOVA BOOTSTRAP Support vector machines
• 年，卷（期）: 2020,（2）
• 页码范围: 62-75
• 页数: 14页
• 分类号: TP181

摘要

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.