- author = "James Cunha Werner and Tatiana Kalganova",
- title = "Risk Evaluation using Evolvable Discriminate Function",
- booktitle = "The {ECML/PKDD-2003} Discovery Challenge Workshop",
- year = "2003",
- editor = "Petr Berka",
- pages = "120--134",
- address = "Cavtat-Dubrovnik, Croatia",
- month = sep # " 23",
- keywords = "genetic algorithms, genetic programming, medical diagnostic",
-
URL = "
http://www.geocities.com/jamwer2002/arte1.pdf",
- size = "12 pages",
- abstract = "This essay proposes a new approach to risk evaluation using disease mathematical modelling. 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 behaviour (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.",
-
notes = "http://lisp.vse.cz/challenge/ecmlpkdd2003/chall2003.htm
14th European Conference on Machine Learning and 7th European Conference on Principles and Practice of Knowledge Discovery in Databases ECML/PKDD-2003",
