Abstract:
A rough-sets approach was applied to a data set consisting of animal study results
and other compound characteristics to generate local and global (certain/possible)
sets of rules for prediction of developmental toxicity in human subjects. A modified
version of the rough-sets approach is proposed to allow the construction of an approximate
set of rules to use for prediction in a manner similar to that of discriminant analysis.
The modified rough-sets approach is superior in predictability to the original form
of rough-sets methodology. In comparison to discriminant analysis, modified rough
sets (approximate rules) appear to be better in overall classification, sensitivity,
positive and negative predictive values. The findings were supported by applying the
modified rough sets and discriminant analysis on a test data set generated from the
original data set by using a resampling plan.
Key-Words
Risk Assessment - Rough Sets - Rule Learning - Information Systems