Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection
Abstract
:1. Introduction
2. Materials and Methods
2.1. Ethics
2.2. Study Participants
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- known active malignant diseases other than gastric cancer,
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- ongoing neoadjuvant chemotherapy,
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- a history of stomach surgery (except vagotomy and ulcer suturing),
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- inflammatory bowel disease (Crohn’s disease and ulcerative colitis),
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- end-stage renal insufficiency,
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- diabetes mellitus type I,
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- active bronchial asthma, and
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- a history of small bowel resections.
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- fast for at least 12 h;
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- refrain from drinking coffee, tea and soft drinks for at least 12 h;
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- refrain from smoking for at least two hours;
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- avoid alcohol for at least 24 h;
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- do not clean your teeth within two hours before the procedure (no brushing, no mouthwash, no flossing if the floss has any aroma);
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- avoid chewing gum and using any mouth fresheners for at least 12 h;
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- refrain from using cosmetics/fragrances on the day of the test prior to the procedure;
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- avoid excessive physical activity (the gym, jogging, cycling, intense physical work) for at least two hours prior to the test.
2.3. Breath Measurement
2.4. Data Analysis
2.4.1. Preprocessing of the Raw Data
2.4.2. Clustering of the Measurements
2.4.3. Classification
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Feature | Overall Accuracy | Sensitivity | Specificity |
---|---|---|---|
Minimum | 72.18% (71.49–72.87%) | 46.9% (45.39–48.41%) | 85.51% (84.76–86.26%) |
Average | 74.21% (73.5–74.91%) | 51.85% (50.35–53.34%) | 86.02% (85.27–86.76%) |
Maximum | 73.7% (72.96–74.44%) | 53.44% (51.94–54.94%) | 84.38% (83.6–85.16%) |
Average of the last 10 time points | 73.74% (73.02–74.45%) | 53.00% (51.51–54.49%) | 84.67% (83.9–85.44%) |
Area under the curve | 73.75% (73.04–74.47%) | 50.77% (49.28–52.26%) | 85.88% (85.13–86.64%) |
Cluster (DTWARP distance, Ward linkage, InfoGain) | 77.81% (77.15–78.48%) | 64.05% (62.66–65.44%) | 85.04% (84.29–85.78%) |
Cluster (Euclidean distance, Ward linkage, Symm.Unc.) | 77.1% (76.41–77.79%) | 66.54% (65.21–67.87%) | 82.64% (81.83–83.45%) |
Feature | Overall Accuracy | Sensitivity | Specificity |
---|---|---|---|
Minimum | 69.89% (69.19–70.59%) | 45.3% (43.87–46.73%) | 82.93% (82.12–83.74%) |
Average | 70.58% (69.86–71.29%) | 47% (45.56–48.45%) | 83.13% (82.3–83.95%) |
Maximum | 70.33% (69.66–71.01%) | 43.23% (41.82–44.64%) | 84.72% (83.95–85.49%) |
Average of the last 10 time points | 70.97% (70.25–71.69%) | 48.79% (47.32–50.25%) | 82.78% (81.96–83.6%) |
Area under the curve | 70.51% (69.79–71.23%) | 46.54% (45.1–47.99%) | 83.27% (82.44–84.1%) |
Cluster (DTWARP distance, Ward linkage, ReliefF) | 75.01% (74.39–75.63%) | 46.52% (45.13–47.91%) | 90.12% (89.5–90.74%) |
Cluster (Euclidean distance, complete linkage, ReliefF) | 74.51% (73.90–75.13%) | 46.04% (44.68–47.41%) | 89.66% (89.01–90.30%) |
Feature | Overall Accuracy | Sensitivity | Specificity |
---|---|---|---|
Minimum | 73.84% (73.23–74.45%) | 41.31% (39.99–42.64%) | 91.14% (90.51–91.78%) |
Maximum | 73.45% (72.79–74.11%) | 45.72% (44.31–47.14%) | 88.2% (87.53–88.88%) |
Average | 74.26% (73.64–74.87%) | 43.16% (41.77–44.55%) | 90.74% (90.12–91.37%) |
Average of the last 10 time points | 75.1% (74.47–75.74%) | 48.33% (46.94–49.71%) | 89.27% (88.62–89.92%) |
Area under the curve | 72.75% (72.13–73.37%) | 40.86% (39.48–42.25%) | 89.68% (89.02–90.33%) |
Cluster (DTWARP distance, complete linkage, InfoGain) | 74.87% (74.16–75.59%) | 60.73% (59.31–62.15%) | 82.48% (81.66–83.30%) |
Cluster (Euclidean distance, Ward linkage, InfoGain) | 73.86% (73.07–74.65%) | 61.05% (59.52–62.58%) | 80.72% (79.84–81.6%) |
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Polaka, I.; Bhandari, M.P.; Mezmale, L.; Anarkulova, L.; Veliks, V.; Sivins, A.; Lescinska, A.M.; Tolmanis, I.; Vilkoite, I.; Ivanovs, I.; et al. Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection. Diagnostics 2022, 12, 491. https://doi.org/10.3390/diagnostics12020491
Polaka I, Bhandari MP, Mezmale L, Anarkulova L, Veliks V, Sivins A, Lescinska AM, Tolmanis I, Vilkoite I, Ivanovs I, et al. Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection. Diagnostics. 2022; 12(2):491. https://doi.org/10.3390/diagnostics12020491
Chicago/Turabian StylePolaka, Inese, Manohar Prasad Bhandari, Linda Mezmale, Linda Anarkulova, Viktors Veliks, Armands Sivins, Anna Marija Lescinska, Ivars Tolmanis, Ilona Vilkoite, Igors Ivanovs, and et al. 2022. "Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection" Diagnostics 12, no. 2: 491. https://doi.org/10.3390/diagnostics12020491