Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Cancer diagnosis and prognosis via linear-programming-based machine learning
Cancer diagnosis and prognosis via linear-programming-based machine learning
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
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
Fuzzy expert system for predicting pathological stage of prostate cancer
Expert Systems with Applications: An International Journal
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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. 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%.