title: Knowledge-guided mutation in classification rules for autism treatment efficacy.
abstract: Data mining methods in biomedical research might benefit by combining genetic algorithms with domain-specific knowledge. The objective of this research is to show how the evolution of treatment rules for autism might be guided. The semantic distance between two concepts in the taxonomy is measured by the number of relationships separating the concepts in the taxonomy. The hypothesis is that replacing a concept in a treatment rule will change the accuracy of the rule in direct proportion to the semantic distance between the concepts. The method uses a patient database and autism taxonomies. Treatment rules are developed with an algorithm that exploits the taxonomies. The results support the hypothesis. This research should both advance the understanding of autism data mining in particular and of knowledge-guided evolutionary search in biomedicine in general.
authors: Harrisburg Area Community College (HACC), USA.
authors: Kelley
authors: K
authors: Engle
authors: University of Maryland, Baltimore County (UMBC), USA.
authors: Roy
authors: R
authors: Rada
mesh_terms: D000465:Algorithms; D001321:Autistic Disorder / Q000628:therapy*; D002965:Classification / Q000379:methods; D002985:Clinical Protocols / Q000145:classification*; D057225:Data Mining / Q000379:methods; D006801:Humans; D019359:Knowledge; D012660:Semantics; D016896:Treatment Outcome
keywords: autism; classification rules; genetic algorithms; medical domain knowledge