Analysis of Autism Prevalence and Neurotoxins Using Combinatorial Fusion and Association Rule Mining

Author

Schweikert, Christina ; Li, Yanjun ; Dayya, David ; Yens, David ; Torrents, Martin ; Hsu, D. Frank

Author_Institution

Dept. of Comput. & Inf. Sci., Fordham Univ., Bronx, NY, USA

fYear

2009

fDate

22-24 June 2009

Firstpage

400

Lastpage

404

Abstract

The increase in autism prevalence has been the motivation for much research which has produced various theories for its causation. Genetic and environmental factors have been investigated. An area of focus is the affect of exposure to neurotoxins, such as mercury and lead, during critical stages in a childpsilas early development. In this study we apply Combinatorial Fusion Analysis (CFA) and Association Rule Mining (ARM) to autism prevalence, mercury, and lead data to generate hypotheses and explore possible associations.

Keywords

bioinformatics; brain; data mining; lead; medical computing; medical disorders; mercury (metal); neurophysiology; toxicology; Hg; Pb; association rule mining; autism prevalence analysis; bioinformatics; combinatorial fusion analysis; data mining; multiple scoring systems; neurotoxins; rank-score characteristic graph; Association rules; Autism; Bioinformatics; Data mining; Environmental factors; Genetics; Information analysis; Pediatrics; USA Councils; Variable speed drives; Association Rule Mining; Combinatorial Fusion Analysis (CFA); Data Mining; Information Fusion; Multiple Scoring Systems; Rank-Score Characteristic (RSC) graph; autism; lead; mercury; neurotoxins;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioinformatics and BioEngineering, 2009. BIBE '09. Ninth IEEE International Conference on

Conference_Location

Taichung

Print_ISBN

978-0-7695-3656-9

Type

conf

DOI

10.1109/BIBE.2009.69

Filename

5211234