Prediction of breast cancer biopsy outcomes using a distributed genetic programming approach

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

@InProceedings{Ludwig:2010:IHIS,
  author =       "Simone A. Ludwig",
  title =        "Prediction of breast cancer biopsy outcomes using a
                 distributed genetic programming approach",
  booktitle =    "Proceedings of the 1st ACM International Health
                 Informatics Symposium",
  editor =       "Tiffany C. Veinot and 
                 {\"U}mit V. {\c C}ataly{\"u}rek and Gang Luo and Henrique Andrade and 
                 Neil R. Smalheiser",
  year =         "2010",
  pages =        "694--699",
  address =      "Arlington, Virginia, USA",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, benign,
                 cancer recurrence, classification, malignant",
  isbn13 =       "978-1-4503-0030-8",
  DOI =          "doi:10.1145/1882992.1883099",
  size =         "6 pages",
  acmid =        "1883099",
  abstract =     "Worldwide, breast cancer is the second most common
                 type of cancer after lung cancer and the fifth most
                 common cause of cancer death accounting for 519,000
                 deaths worldwide in 2004. The most effective method for
                 breast cancer screening today is mammography. However,
                 presently predictions of breast biopsies resulting from
                 mammogram interpretation lead to approximately
                 70percent biopsies with benign outcomes, which are
                 preventable. Therefore, an automatic method is
                 necessary to aid physicians in the prognosis of
                 mammography interpretations. The data set used for this
                 investigation is based on BI-RADS findings. Previous
                 work has achieved good results using a decision tree,
                 an artificial neural networks and a case-based
                 reasoning approach to develop predictive classifiers.
                 This paper uses a distributed genetic programming
                 approach to predict the outcomes of the mammography
                 achieving even better prediction results.",
}

Genetic Programming entries for Simone A Ludwig

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