Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/2517
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dc.contributor.authorWerner, J C-
dc.contributor.authorKalganova, T-
dc.coverage.spatial10en
dc.date.accessioned2008-07-21T15:31:44Z-
dc.date.available2008-07-21T15:31:44Z-
dc.date.issued2003-
dc.identifier.citationProceeding of the 6th European Conference on Genetic Programming, EuroGP2003, Essex, UK, 2003. vol. 2610, pp. 465-473en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/2517-
dc.description.abstractPrecocious 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.en
dc.format.extent647268 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherSpringeren
dc.titleDisease modelling using evolved discriminate functionen
dc.typeConference Paperen
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Electrical Engineering Research Papers

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