Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection.
VOCs
XGBoost
algorithms
breath acetone
diabetes
e-nose
machine learning
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
18 Jun 2021
18 Jun 2021
Historique:
received:
06
06
2021
revised:
13
06
2021
accepted:
17
06
2021
entrez:
2
7
2021
pubmed:
3
7
2021
medline:
7
7
2021
Statut:
epublish
Résumé
Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.
Identifiants
pubmed: 34207196
pii: s21124187
doi: 10.3390/s21124187
pmc: PMC8234852
pii:
doi:
Substances chimiques
Acetone
1364PS73AF
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Narodowym Centrum Nauki
ID : 2017/26/D/ST7/00355
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