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 2024-01-20T12:34:52Z 2024-01-20T12:34:52Z 2022-01-25 2013-03 1738-642X https://pubs.kist.re.kr/handle/201004/128266 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%. English KOREAN SOCIETY TOXICOGENOMICS & TOXICOPROTEOMICS-KSTT Decision supporting frame to estimate chronic exposure suspicion to VOC chemicals using mixed statistical model Article 10.1007/s13273-013-0011-6 1 MOLECULAR & CELLULAR TOXICOLOGY, v.9, no.1, pp.75 - 83 MOLECULAR & CELLULAR TOXICOLOGY 9 1 75 83 N scie scopus kci other 000317532500011 2-s2.0-84875834250 Biochemistry & Molecular Biology Toxicology Biochemistry & Molecular Biology Toxicology Article Decision supporting system Discriminant analysis VOC Cross-validation