Abstract 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.

中文翻译：

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使用ANOVA-BOOTSTRAP-SVM检测恶性和良性乳腺癌

摘要目的本研究的目的是提出一种改进的ANOVA-SVM方法，以提高检测良性和恶性乳腺癌的准确性。方法论我们提出了一种新方法ANOVA-BOOTSTRAP-SVM。它涉及将方差分析（ANOVA）应用到支持向量机（SVM），但是我们使用引导程序而不是交叉验证作为训练/测试拆分过程。我们已经调整了内核和C参数，并在一组乳腺癌数据集上测试了我们的算法。结果通过使用提出的新方法，我们成功地将精度提高了4.5个百分点到8个百分点，具体取决于数据集。研究局限性该算法对内核类型和优化参数C的值敏感。实际意义我们相信ANOVA-BOOTSTRAP-SVM不仅可以用于识别乳腺癌的类型，而且可以用于对所有类型的癌症进行更广泛的研究。创新性/价值我们的发现很重要，因为与标准版本的支持向量机相比，该算法可以更准确地检测各种类型的癌症。