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Health Informatics Journal
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*Prostate Cancer
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ProICET: a cost-sensitive system for prostate cancer data

Camelia Vidrighin

Technical University of Cluj-Napoca 26 Baritiu St Cluj-Napoca, Romania, Camelia.Vidrighin{at}cs.utcluj.ro

Rodica Potolea

Technical University of Cluj-Napoca 26 Baritiu St Cluj-Napoca, Romania, Rodica.Potolea{at}cs.utcluj.ro

Cancer is the second most threatening disease in the world today, not only because of its mortality rate, but also due to the brutal changes it imposes on the patient's life, and the fact that its exact causes of progression remain to be discovered. Recent evolution in computer technology has resulted in the emergence of a combined approach to the diagnosis and prognosis process, with a data driven analytical approach complementing biomedical and clinical methods. Cost-sensitive learning is one such data mining method, particularly well suited for medical problems. This paper investigates the performance of a new system based on a hybrid cost-sensitive algorithm (ProICET) on a prostate cancer medical dataset, while trying to produce new medical knowledge. The target of such a system is to reduce the total cost while keeping a high classification accuracy.

Key Words: cost-sensitive learning • data mining • hybrid algorithm • implementation • prostate cancer

Health Informatics Journal, Vol. 14, No. 4, 297-307 (2008)
DOI: 10.1177/1460458208096558
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