Lung cancer detection via breath by electronic nose enhanced with a sparse group feature selection approach

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

“…This behavior has become one of the strongest limitations of the sensor array technology based on MOS sensors. This unstable signal baseline, combined with the mixture complexity of VOCs contained in a human breath sample, results in high challenges when developing chemometric model selection to differentiate between positive (P) and negative (N) COVID-19 patterns [52] , [63] , [64] , [65] , [66] . However, to suppress this effect at the minimum level, GeNose C19 can be preconditioned and placed in a location where the environmental condition is relatively stable (e.g., indoor).…”

“…In a chemometric study on an electronic tongue consisting of 16 sensors for dairy product discrimination, utilizing a linear discriminant analysis combined with a simulated annealing (SA) feature-selection algorithm could possibly produce an accurate model based on signals from only four sensors [51] . For breath analysis using an e-nose, combining the sparse group lasso (SGL) feature selection with a support vector machine (SVM) improves the classification performance in differentiating patients with lung cancer from healthy subjects and patients with benign pulmonary diseases [52] . However, despite its ability to enhance the classification of up to 12%, this SGL feature selection approach performs dimensionality reduction by eliminating several data rather than grouping and scoring for the whole data, leading to a poor discriminatory predictive performance [52] , [53] .…”

“…For breath analysis using an e-nose, combining the sparse group lasso (SGL) feature selection with a support vector machine (SVM) improves the classification performance in differentiating patients with lung cancer from healthy subjects and patients with benign pulmonary diseases [52] . However, despite its ability to enhance the classification of up to 12%, this SGL feature selection approach performs dimensionality reduction by eliminating several data rather than grouping and scoring for the whole data, leading to a poor discriminatory predictive performance [52] , [53] . Moreover, the maintenance of the e-nose performance using few sensors had not been demonstrated in that study.…”

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose

“…We can also introduce the breath sample without using the BioVOC TM but utilising a pump electronically controlled to identify the tidal-wave breath final phase that is introduced into the gas chamber. Further improvement can be reached by applying preconcentration of the analysed VOCs, as presented elsewhere, but requiring a bulkier and more energy-consuming setup 38 . Some of these underlined detrimental effects can be corrected with simple changes, for more accurate control of the flow of the breath sample.…”

The Research of Chronic Gastritis Diagnosis with Electronic Noses

“…The application of e-nose in medical diagnosis especially has attracted more and more interest of the researchers. Liu et al developed an e-nose system for lung cancer detection via breath with a sparse group feature selection approach and attained better results with 94.2% accuracy, 97.8% sensitivity, and 90.2% sensitivity [15]. Raspagliesi et al tested the feasibility of e-nose as a diagnostic tool for ovarian cancer, the performance tested in the prediction gave 98% of sensitivity and 95% of specificity [16].…”