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.

Sandeep Chaurasia 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(IJARAI), 2(9), 2013. http://dx.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},
doi = {10.14569/IJARAI.2013.020907},
url = {http://dx.doi.org/10.14569/IJARAI.2013.020907},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {9},
author = {Sandeep Chaurasia and Dr. P Chakrabarti}
}