Author
Abstract
A new, evolutionary computation-based approach to discovering prediction models in surveillance data was developed and evaluated. This approach was operationalized in EpiCS, a type of learning classifier system specially adapted to model clinical data. In applying EpiCS to a large, prospective injury surveillance database, EpiCS was found to create accurate predictive models quickly that were highly robust, being able to classify > 99% of cases early during training. After training, EpiCS classified novel data more accurately (p < 0.001) than either logistic regression or decision tree induction (C4.5), two traditional methods for discovering or building predictive models.
Year of Publication
2000
Journal
Proc AMIA Symp
Number of Pages
359-63
Date Published
2000
ISSN Number
1531-605X
Alternate Journal
Proc AMIA Symp
PMID
11079905