Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Machine Learning
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
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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 70% 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.