- author = "Stephan M. Winkler and Michael Affenzeller and Witold Jacak and Herbert Stekel",
- title = "Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system",
- booktitle = "GECCO 2011 Medical applications of genetic and evolutionary computation (MedGEC)",
- year = "2011",
- editor = "Stephen L. Smith and Stefano Cagnoni and Robert Patton",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming",
- pages = "503--510",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2002040",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "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 tumours we have trained mathematical
models for estimating cancer diagnoses.
Several data based modelling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbour learning, artificial neural networks, and support vector machines (all optimised 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 81percent, 74percent, and 91percent of the analysed test cases, respectively; without tumour markers up to 75percent, 74percent, and 87percent of the test samples are correctly estimated, respectively.",
-
notes = "Also known as \cite{2002040} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
