Please use this identifier to cite or link to this item:
Title: Risk evaluation using evolvable discriminate function
Authors: Werner, JC
Kalganova, T
Keywords: Risk evaluation;Disease mathematical modeling;Database
Issue Date: 2003
Citation: Proc. Of The ECML/PKDD-2003 Discovery Challenge Workshop. 14th European Conference on Machine Learning and 7th European Conference on Principles and Practice of Knowledge Discovery in Databases ECML/PKDD-2003 Ed: 120-134, Petr Berka, Croatia, 2003
Abstract: This essay proposes a new approach to risk evaluation using disease mathematical modeling. The mathematical model is an algebraic equation of the available database attributes and is used to evaluate the patient condition. If its value is greater than zero it means that the patient is ill (or in risk condition), otherwise healthy. In practice risk evaluation has been a very difficult problem mainly due its sporadic behavior (suddenly, the patient has a stroke, etc as a condition aggravation) and its database representation. The database contains, under the label of risk patient data, information of the patient condition that sometimes is in risk condition and sometimes is not, introducing errors in the algorithm training. The study was applied to Atherosclerosis database from Discovery Challenge 2003 - ECML/PKDD 2003 workshop.
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf609.12 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.