Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/2517
Title: Disease modelling using evolved discriminate function
Authors: Werner, J C
Kalganova, T
Issue Date: 2003
Publisher: Springer
Citation: Proceeding of the 6th European Conference on Genetic Programming, EuroGP2003, Essex, UK, 2003. vol. 2610, pp. 465-473
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://bura.brunel.ac.uk/handle/2438/2517
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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