RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis
Badal Soni1, Angshuman Bora2, Arpita Ghosh3, and Anji Reddy4

1Badal Soni*, Computer Science and Engineering,National Institute of Technology Silchar, Assam, India.
2Angshuman Bora, Computer Science and Engineering,National Institute of Technology Silchar, Assam, India.
3Arpita Ghosh, Computer Science and Engineering, National Institute of Technology Silchar, Assam, India.
4Anji Reddy,Computer Science and Engineering, Lendi Institute of Engineering and Technology, Andhra Pradesh, India.

Manuscript received on September 15, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3295-3305 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28081081219/2019©BEIESP | DOI: 10.35940/ijitee.L2808.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cancer is a disease, which develops, in human body due to gene mutation. Due to various factor cells turn into cancerous cell and grow rapidly while damaging normal cells. Many women get affected by breast cancer, which might even cause death if not treated at early stage. Early detection of breast cancer is highly important to increase the survival rate. Machine learning methods and technologies are making it possible to classify and detect the class in an accurate manner. Among other classifiers, random forest and support vector machine are two classifiers that have a good classification power. In this, research a combination of these two classifier i.e. Random Forest and Support Vector Machine (RFSVM) is proposed for early diagnosis of breast cancer cell using Wisconsin Breast Cancer Dataset (WBCD). Using different train-test data ratio experiments are performed and an average of more than 98percentage accuracy is achieved using this hybrid classifier. This paper overcomes the over-fitting problem of random forest and the need of tuning the parameters of Support Vector Machine. Even with limited data available, the classifier tunes its parameters so well to give a highly accurate result.
Keywords: Random Forest, Support Vector Machine, Breast Cancer, Classification, Feature Extraction.
Scope of the Article: Classification