NDLI: Decision supporting frame to estimate chronic exposure suspicion to VOC chemicals using mixed statistical model

Content Provider	Springer Nature Link
Author	Kang, Byeong Chul An, Yu Ri Kang, Yeon Kyung Shin, Ga Hee Kim, Seung Jun Hwang, Seong Yong Nam, Suk Woo Ryu, Jae Chun Park, Jun Hyung
Copyright Year	2013
Abstract	In this paper, we examine the model for a chemical exposure decision support algorithm. Our purpose is to suggest the model frame to describe possibility of exposure with low-dose VOC chemicals for long time under normal circumstances at working place. Forensic rhetoric terms, non-exclusion exposure suspicion (NES) and exclusion exposure suspicion (EES), were defined and various statistical methods were combined basis of Bayesian approach. Decisiontree (DT) methods of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and naïve Bayes model were evaluated to classify 3 VOCs (toluene, xylene, and ehtybenzene) by means of the results of urinary test, gene expression and methylation expression experiments. Overall procedure is conducted by leave-one-out cross-validation that error rate of NES resulted in 11%.
Starting Page	75
Ending Page	83
Page Count	9
File Format	PDF
ISSN	1738642X
Journal	Molecular & Cellular Toxicology
Volume Number	9
Issue Number	1
e-ISSN	20928467
Language	English
Publisher	The Korean Society of Toxicogenomics and Toxicoproteomics
Publisher Date	2013-04-03
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Decision supporting system Discriminant analysis VOC Cross-validation Cell Biology Pharmacology/Toxicology
Content Type	Text
Resource Type	Article
Subject	Health, Toxicology and Mutagenesis Public Health, Environmental and Occupational Health Toxicology Pathology and Forensic Medicine

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