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 %.
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Ludwig, S.A., Roos, S. (2010). Prognosis of Breast Cancer Using Genetic Programming. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15384-6_57
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DOI: https://doi.org/10.1007/978-3-642-15384-6_57
Publisher Name: Springer, Berlin, Heidelberg
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