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

• Published in: Journal of breath research Vol. 16; no. 3; pp. 36001 - 36012
• Main Authors: Xuan, Wufan, Zheng, Lina, Bunes, Benjamin R, Crane, Nichole, Zhou, Fubao, Zang, Ling
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.07.2022
• Subjects: breath test; Breath Tests - methods; Cross-Sectional Studies; Electronic Nose; ensemble learning; Exhalation; Humans; nanofiber sensor; silicosis; Silicosis - diagnosis; ensemble learning; silicosis; electronic nose; breath test; nanofiber sensor
• ISSN: 1752-7155; 1752-7163; 1752-7163
• DOI: 10.1088/1752-7163/ac5f13

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.