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Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breat...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871298/ https://www.ncbi.nlm.nih.gov/pubmed/35204584 http://dx.doi.org/10.3390/diagnostics12020491 |
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author | Polaka, Inese Bhandari, Manohar Prasad Mezmale, Linda Anarkulova, Linda Veliks, Viktors Sivins, Armands Lescinska, Anna Marija Tolmanis, Ivars Vilkoite, Ilona Ivanovs, Igors Padilla, Marta Mitrovics, Jan Shani, Gidi Haick, Hossam Leja, Marcis |
author_facet | Polaka, Inese Bhandari, Manohar Prasad Mezmale, Linda Anarkulova, Linda Veliks, Viktors Sivins, Armands Lescinska, Anna Marija Tolmanis, Ivars Vilkoite, Ilona Ivanovs, Igors Padilla, Marta Mitrovics, Jan Shani, Gidi Haick, Hossam Leja, Marcis |
author_sort | Polaka, Inese |
collection | PubMed |
description | Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications. |
format | Online Article Text |
id | pubmed-8871298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88712982022-02-25 Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection Polaka, Inese Bhandari, Manohar Prasad Mezmale, Linda Anarkulova, Linda Veliks, Viktors Sivins, Armands Lescinska, Anna Marija Tolmanis, Ivars Vilkoite, Ilona Ivanovs, Igors Padilla, Marta Mitrovics, Jan Shani, Gidi Haick, Hossam Leja, Marcis Diagnostics (Basel) Article Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications. MDPI 2022-02-14 /pmc/articles/PMC8871298/ /pubmed/35204584 http://dx.doi.org/10.3390/diagnostics12020491 Text en © 2022 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 Polaka, Inese Bhandari, Manohar Prasad Mezmale, Linda Anarkulova, Linda Veliks, Viktors Sivins, Armands Lescinska, Anna Marija Tolmanis, Ivars Vilkoite, Ilona Ivanovs, Igors Padilla, Marta Mitrovics, Jan Shani, Gidi Haick, Hossam Leja, Marcis Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection |
title | Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection |
title_full | Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection |
title_fullStr | Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection |
title_full_unstemmed | Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection |
title_short | Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection |
title_sort | modular point-of-care breath analyzer and shape taxonomy-based machine learning for gastric cancer detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871298/ https://www.ncbi.nlm.nih.gov/pubmed/35204584 http://dx.doi.org/10.3390/diagnostics12020491 |
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