ArticleArtificial intelligence in medicine2022
Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.
Article in Artificial intelligence in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds.Diagnostic microbiology and infectious disease · 2024Pooled it
- Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers.International journal of molecular sciences · 2023Pooled it
- Development of a rapid metal oxide semiconductor-based sensory system for noninvasive neonatal sepsis detection.Mikrochimica acta · 2026Article
- A Few-Shot SE-Relation Net-Based Electronic Nose for Discriminating COPD.Sensors (Basel, Switzerland) · 2025Article
- Risk-based evaluation of machine learning-based classification methods used for medical devices.BMC medical informatics and decision making · 2025Article
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath.Scientific reports · 2024Article
- Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.Expert systems with applications · 2023Article
- Enhanced real-time mass spectrometry breath analysis for the diagnosis of COVID-19.ERJ open research · 2023Article
- Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication.NPJ science of food · 2023Article
- Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.Sensors (Basel, Switzerland) · 2023Review
- Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.
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