Risk Evaluation using Evolvable Discriminate
Function
J. Werner, and T. Kalganova. The ECML/PKDD-2003 Discovery Challenge Workshop, page 120--134. Cavtat-Dubrovnik, Croatia, (September 2003)
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.
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
%0 Conference Paper
%1 werner:2003:PKDD
%A Werner, James Cunha
%A Kalganova, Tatiana
%B The ECML/PKDD-2003 Discovery Challenge Workshop
%C Cavtat-Dubrovnik, Croatia
%D 2003
%E Berka, Petr
%K algorithms, diagnostic genetic medical programming,
%P 120--134
%T Risk Evaluation using Evolvable Discriminate
Function
%U http://www.geocities.com/jamwer2002/arte1.pdf
%X 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.
@inproceedings{werner:2003:PKDD,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Cavtat-Dubrovnik, Croatia},
author = {Werner, James Cunha and Kalganova, Tatiana},
biburl = {https://www.bibsonomy.org/bibtex/26d43ca13d725a9996cc202488fb906b8/brazovayeye},
booktitle = {The {ECML/PKDD-2003} Discovery Challenge Workshop},
editor = {Berka, Petr},
interhash = {dbbc93057e3d10b84dd6c1e7738d81d6},
intrahash = {6d43ca13d725a9996cc202488fb906b8},
keywords = {algorithms, diagnostic genetic medical programming,},
month = {September 23},
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},
pages = {120--134},
size = {12 pages},
timestamp = {2008-06-19T17:54:05.000+0200},
title = {Risk Evaluation using Evolvable Discriminate
Function},
url = {http://www.geocities.com/jamwer2002/arte1.pdf},
year = 2003
}