Prediction of breast cancer biopsy outcomes using a distributed genetic programming approach
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- @InProceedings{Ludwig:2010:IHIS,
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author = "Simone A. Ludwig",
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title = "Prediction of breast cancer biopsy outcomes using a
distributed genetic programming approach",
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booktitle = "Proceedings of the 1st ACM International Health
Informatics Symposium",
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editor = "Tiffany C. Veinot and
{\"U}mit V. {\c C}ataly{\"u}rek and Gang Luo and Henrique Andrade and
Neil R. Smalheiser",
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year = "2010",
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pages = "694--699",
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address = "Arlington, Virginia, USA",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, benign,
cancer recurrence, classification, malignant",
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isbn13 = "978-1-4503-0030-8",
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DOI = "doi:10.1145/1882992.1883099",
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size = "6 pages",
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acmid = "1883099",
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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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