title: Developmental toxicity risk assessment: a rough sets approach.
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.
authors: Department of Computer and Information Science, University of Arkansas, Little Rock.
authors: R R
authors: RR
authors: Hashemi
authors: F R
authors: FR
authors: Jelovsek
authors: M
authors: M
authors: Razzaghi
mesh_terms: D000818:Animals; D002415:Cats; D002417:Cattle; D006224:Cricetinae; D004285:Dogs; D005289:Ferrets; D006041:Goats; D006168:Guinea Pigs; D006801:Humans; D007256:Information Systems; D051379:Mice; D008962:Models, Theoretical; D011817:Rabbits; D051381:Rats; D012306:Risk; D012756:Sheep; D013552:Swine; D014118:Toxins, Biological
chemical_list: D014118:Toxins, Biological