Prognosis of Breast Cancer Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Ludwig:2010:KES,
  title =        "Prognosis of Breast Cancer Using Genetic Programming",
  author =       "Simone A. Ludwig and Stefanie Roos",
  booktitle =    "14th International Conference on Knowledge-Based and
                 Intelligent Information and Engineering Systems (KES
                 2010), Part {IV}",
  year =         "2010",
  editor =       "Rossitza Setchi and Ivan Jordanov and 
                 Robert J. Howlett and Lakhmi C. Jain",
  volume =       "6279",
  series =       "Lecture Notes in Computer Science",
  pages =        "536--545",
  address =      "Cardiff, UK",
  month =        sep # " 8-10",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-15383-9",
  DOI =          "doi:10.1007/978-3-642-15384-6_57",
  size =         "10 pages",
  bibdate =      "2010-12-02",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/kes/kes2010-4.html#LudwigR10",
  abstract =     "Worldwide, breast cancer is the second most common
                 type of cancer after lung cancer and the fifth most
                 common cause of cancer death. In 2004, breast cancer
                 caused 519,000 deaths worldwide. In order to reduce the
                 cancer deaths and thereby increasing the survival rates
                 an automatic approach is necessary to aid physicians in
                 the prognosis of breast cancer. This paper investigates
                 the prognosis of breast cancer using a machine learning
                 approach, in particular genetic programming, whereas
                 earlier work has approached the prognosis using linear
                 programming. The genetic programming method takes a
                 digitized image of a patient and automatically
                 generates the prediction of the time to recur as well
                 as the disease-free survival time. The breast cancer
                 dataset from the University of California Irvine
                 Machine Learning Repository was used for this study.
                 The evaluation shows that the genetic programming
                 approach outperforms the linear programming approach by
                 33 percent.",
  affiliation =  "Department of Computer Science, University of
                 Saskatchewan, Canada",
}

Genetic Programming entries for Simone A Ludwig Stefanie Roos

Citations