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Research Article | Open Access |

An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis

Author 1: Sandeep Chaurasia Author 2: Dr. P Chakrabarti
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 2, No. 9 · Published 2013 · Cited by 8

DOI: https://doi.org/10.14569/IJARAI.2013.020907

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Abstract

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of machine learning. In this paper we have used an approach by using support vector machine classifier to construct a model that is useful for the breast cancer survivability prediction. We have used both 5 cross and 10 cross validation of variable selection on input feature vectors and the performance measurement through bio-learning class performance while measuring AUC, specificity and sensitivity. The performance of the SVM is much better than the other machine learning classifier.

Keywords

Breast cancer feature selection Support vectors Support Vector Machine Wisconsin Breast Cancer Dataset.

How to Cite this Article

Chaurasia, S., & Chakrabarti, D. P. (2013). An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis. International Journal of Advanced Research in Artificial Intelligence, 2(9). https://doi.org/10.14569/IJARAI.2013.020907

Chaurasia, Sandeep, and Dr. P Chakrabarti. "An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis." International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 9, 2013, https://doi.org/10.14569/IJARAI.2013.020907.

@article{Chaurasia2013,
  title     = {An Approach with Support Vector Machine using Variable Features Selection on Breast Cancer Prognosis},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {2},
  number    = {9},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Sandeep Chaurasia and Dr. P Chakrabarti},
  doi       = {10.14569/IJARAI.2013.020907},
  url       = {https://doi.org/10.14569/IJARAI.2013.020907}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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