Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9776
Title: Disease modeling using Evolved Discriminate Function
Authors: Werner, JC
Kalganova, T
Keywords: Genetic programming;Discriminate function;Disease dynamics;Breast cancer;Thrombosis & Collagen diseases;Numerical clinical data
Issue Date: 2003
Publisher: Springer-Verlag
Citation: European Conference on Genetic Programming, 2610 pp. 465 - 473, 2003
Abstract: Precocious diagnosis increases the survival time and patient quality of life. It is a binary classification, exhaustively studied in the literature. This paper innovates proposing the application of genetic programming to obtain a discriminate function. This function contains the disease dynamics used to classify the patients with as little false negative diagnosis as possible. If its value is greater than zero then it means that the patient is ill, otherwise healthy. A graphical representation is proposed to show the influence of each dataset attribute in the discriminate function. The experiment deals with Breast Cancer and Thrombosis & Collagen diseases diagnosis. The main conclusion is that the discriminate function is able to classify the patient using numerical clinical data, and the graphical representation displays patterns that allow understanding of the model.
URI: http://link.springer.com/chapter/10.1007%2F3-540-36599-0_44
http://bura.brunel.ac.uk/handle/2438/9776
DOI: http://dx.doi.org/10.1007/3-540-36599-0_44
ISBN: 978-3-540-00971-9
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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