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

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@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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