Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer (BC) Dataset
Rajesh Kumar Maurya1, Sanjay Kumar Yadav2, Shwata Agrawal3

1Rajesh Kumar Maurya, Department of CS & IT, SHUATS Allahabad (U.P), India.
2Sanjay Kumar Yadav, Department of CS & IT, SHUATS Allahabad (U.P), India.
3Shwata Agrawal, Department of MCA, ABES Engineering College, NH Ghaziabad (U.P), India.
Manuscript received on 04 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 05 September 2019 | PP: 412-417 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10760782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1076.0782S719
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Abstract: Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming to economically developing country like India. Government of India made a lot of effort to make aware the women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but their performance varies with the kind of data available. In this study we, apply four different Kernels such as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel (RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with respect to accuracy, runtime, specificity and precision. The investigations outcome displays that RBFK provides greater accuracy with minimal errors.
Keywords: BC Causes, BC Problems, Challenges, ML Techniques, SVM-K, Efficiency, Precision, Accuracy, Run Time, Specificity, Confusion Matrix.
Scope of the Article: Machine Learning