Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

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 s...

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Bibliographic Details
Published in:Diagnostics (Basel) 2022-02, Vol.12 (2), p.491
Main Authors: 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
Format: Article
Language:English
Subjects:
breath analysis
Data analysis
Disease
electronic nose
Endoscopy
Gastric cancer
Laboratories
Ligands
Machine learning
Metabolites
Metadata
Mortality
Patients
Questionnaires
screening
Sensors
VOCs
Volatile organic compounds
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Summary: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). 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. 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. 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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics12020491