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Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection

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 exhale...

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Detalles Bibliográficos
Autores principales: Paleczek, Anna, Grochala, Dominik, Rydosz, Artur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234852/
https://www.ncbi.nlm.nih.gov/pubmed/34207196
http://dx.doi.org/10.3390/s21124187
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author Paleczek, Anna
Grochala, Dominik
Rydosz, Artur
author_facet Paleczek, Anna
Grochala, Dominik
Rydosz, Artur
author_sort Paleczek, Anna
collection PubMed
description 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.
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spelling pubmed-82348522021-06-27 Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection Paleczek, Anna Grochala, Dominik Rydosz, Artur Sensors (Basel) Article 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. MDPI 2021-06-18 /pmc/articles/PMC8234852/ /pubmed/34207196 http://dx.doi.org/10.3390/s21124187 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Paleczek, Anna
Grochala, Dominik
Rydosz, Artur
Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection
title Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection
title_full Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection
title_fullStr Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection
title_full_unstemmed Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection
title_short Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection
title_sort artificial breath classification using xgboost algorithm for diabetes detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234852/
https://www.ncbi.nlm.nih.gov/pubmed/34207196
http://dx.doi.org/10.3390/s21124187
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