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

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%.