Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Classification of tumor marker values using heuristic data mining methods
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
On the architecture and implementation of tree-based genetic programming in HeuristicLab
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper we present results of empirical research work done on the data based identification of estimation models for cancer diagnoses: Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumors we have trained mathematical models for estimating cancer diagnoses. Several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbor learning, artificial neural networks, and support vector machines (all optimized using evolutionary algorithms) as well as genetic programming. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81%, 74%, and 91% of the analyzed test cases, respectively; without tumor markers up to 75%, 74%, and 87% of the test samples are correctly estimated, respectively.