Decision supporting frame to estimate chronic exposure suspicion to VOC chemicals using mixed statistical model
- Authors
- 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
- Issue Date
- Mar-2013
- Publisher
- 대한독성 유전단백체 학회
- Keywords
- Decision supporting system; Discriminant analysis; VOC; Cross-validation
- Citation
- Molecular & Cellular Toxicology, v.9, no.1, pp.75 - 83
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- Molecular & Cellular Toxicology
- Volume
- 9
- Number
- 1
- Start Page
- 75
- End Page
- 83
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/28480
- DOI
- 10.1007/s13273-013-0011-6
- ISSN
- 1738-642X
- 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. Decision-tree (DT) methods of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and naive 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%.
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