Thesis

Genetic Programming and Decision Trees Applied to Medical Data Mining

The digital revolution has brought an explosion of stored data to our world, and data mining, especially on the rapidly expanding medical databases, has the capabilities to turn this information into new and useful medical knowledge. Data mining with cost sensitive decision trees created using Genetic Programming is focused upon, with an emphasis on their relevence and potential in medical data mining. An application that uses elitist multiobjective Genetic Programming to obtain a pareto front of univariate and linear decision trees was implemented, and various mutation operators were tested and compared on their performance in the development of the decision trees. Acknowledgements

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