The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Recruitment Process
2.2. Study Group Description
2.3. Breath Analyzer and Sample Collection
2.4. Data Pre-Processing
- ▪ Minimum value of the curve.
- ▪ Average value of the curve.
- ▪ Maximum value of the curve.
- ▪ Mean value of the last 10 time points to characterize the sensor response after saturation.
- ▪ Area under the curve calculated using the trapezoidal rule.
2.5. Classification
2.6. Dimensionality Reduction
2.7. Experimental Setup
3. Results
3.1. Data Pre-Processing
3.2. Classification 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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Gender | Median Age | Colorectal Cancer Group | Non-Cancer Group | ||||
---|---|---|---|---|---|---|---|
n | % | n | % | n | % | ||
Males | 113 | 39 | 64 | 57 | 54 | 56 | 30 |
Females | 178 | 61 | 63 | 48 | 46 | 130 | 70 |
Total | 291 | 100 | 63 | 105 | 100 | 186 | 100 |
Classification Method | Overall Accuracy | Sensitivity | Specificity | AUC ROC |
---|---|---|---|---|
C4.5 | 60.9% | 46.7% | 68.4% | 0.567 |
Naïve Bayes (NB) | 47.1% | 86.7% | 26.3% | 0.593 |
Artificial Neural Networks (ANNs) | 64.4% | 46.7% | 73.7% | 0.584 |
Random Forest (RF) | 75.9% | 43.3% | 93.0% | 0.684 |
Classification Method | Overall Accuracy | Sensitivity | Specificity | AUC ROC |
---|---|---|---|---|
C4.5 | 65.1% | 65.7% | 64.3% | 0.657 |
Naïve Bayes (NB) | 60.3% | 94.3% | 17.9% | 0.627 |
Artificial Neural Networks (ANNs) | 66.7% | 57.1% | 78.6% | 0.713 |
Random Forest (RF) | 60.3% | 48.6% | 75.0% | 0.658 |
Classification Method | Number of Features | Overall Accuracy | Sensitivity | Specificity | AUC ROC | Feature Selection |
---|---|---|---|---|---|---|
C4.5 | 9 | 77.0% | 63.3% | 84.2% | 0.759 | MOX, Greedy sel. |
Naïve Bayes (NB) | 4 | 71.59% | 34.3% | 96.2% | 0.671 | GNP, Greedy sel. |
Artificial Neural Networks (ANN) | 52 | 72.4% | 46.7% | 86.0% | 0.705 | MOX, Evolutionary |
Random Forest (RF) | 8 | 72.4% | 56.7% | 80.7% | 0.685 | MOX, Greedy sel. |
Classification Method | Number of Features | Overall Accuracy | Sensitivity | Specificity | AUC ROC | Feature Selection |
---|---|---|---|---|---|---|
C4.5 | 9 | 77.0% | 63.3% | 84.2% | 0.759 | MOX, Greedy sel. |
Naïve Bayes (NB) | 1 | 72.4% | 40.0% | 89.5% | 0.711 | All, Greedy sel. |
Neural Networks (NNs) | 5 | 78.2% | 43.3% | 96.5% | 0.735 | All, Greedy sel. |
Random Forest (RF) | 75 | 79.3% | 53.3% | 93.0% | 0.734 | All, Evolutionary |
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Poļaka, I.; Mežmale, L.; Anarkulova, L.; Kononova, E.; Vilkoite, I.; Veliks, V.; Ļeščinska, A.M.; Stonāns, I.; Pčolkins, A.; Tolmanis, I.; et al. The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis. Diagnostics 2023, 13, 3355. https://doi.org/10.3390/diagnostics13213355
Poļaka I, Mežmale L, Anarkulova L, Kononova E, Vilkoite I, Veliks V, Ļeščinska AM, Stonāns I, Pčolkins A, Tolmanis I, et al. The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis. Diagnostics. 2023; 13(21):3355. https://doi.org/10.3390/diagnostics13213355
Chicago/Turabian StylePoļaka, Inese, Linda Mežmale, Linda Anarkulova, Elīna Kononova, Ilona Vilkoite, Viktors Veliks, Anna Marija Ļeščinska, Ilmārs Stonāns, Andrejs Pčolkins, Ivars Tolmanis, and et al. 2023. "The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis" Diagnostics 13, no. 21: 3355. https://doi.org/10.3390/diagnostics13213355