Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners

Abstract

This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored. 398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal component analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (random forest and extreme gradient boosting) and two classical algorithms (K-nearest neighbor and support vector machine). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test. Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817-0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p> 0.05). There was no connection between personal smoking habits and classification accuracy. Breath tests based on an e-nose consisted of 16× sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.

Keywords: breath test; electronic nose; ensemble learning; nanofiber sensor; silicosis.