Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
No Thumbnail Available
Date
2022-02
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
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.
Description
Funding Information: The development of the analysis approach and its evaluation and analysis were supported by a postdoctoral grant within the Activity 1.1.1.2 “Post-doctoral Research Aid” co-funded by the European Regional Development Fund (postdoctoral project numbers: 1.1.1.2/VIAA/2/18/270 and 1.1.1.2/VIAA/3/19/495). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
Breath analysis, Electronic nose, Gastric cancer, Machine learning, Screening, 1.6 Biological sciences, 3.1 Basic medicine, 3.2 Clinical medicine, 1.1. Scientific article indexed in Web of Science and/or Scopus database, Clinical Biochemistry, SDG 3 - Good Health and Well-being
Citation
Polaka, I, Bhandari, M P, Mezmale, L, Anarkulova, L, Veliks, V, Sivins, A, Lescinska, A M, Tolmanis, I, Vilkoite, I, Ivanovs, I, Padilla, M, Mitrovics, J, Shani, G, Haick, H & Leja, M 2022, 'Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection', Diagnostics, vol. 12, no. 2, 491. https://doi.org/10.3390/diagnostics12020491