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

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, e

SUBMITTER: Hidayat SN 

PROVIDER: S-EPMC9110307 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature