Data Pre-Processing of Infrared Spectral Breathprints for Lung Cancer Detection
Bibliographic record
Abstract
Though breath analysis shows promise as a noninvasive and cost-effective approach to lung cancer screening, biomarkers in exhaled breath samples can be overwhelmed by irrelevant internal and environmental volatile organic compounds (VOCs). These extraneous VOCs can obscure the disease signature in a spectral breathprint, hindering the performance of pattern recognition models. In this work, pre-processing pipelines consisting of missing value replacement, detrending, and normalization techniques were evaluated to reduce these effects and enhance the features of interest in infrared cavity ring-down spectra. The best performing pipeline consisted of moving average detrending, linear interpolation for missing values, and vector normalization. This model achieved an average accuracy of 73.04% across five types of classifiers, exhibiting an 8.36% improvement compared to a baseline model (p < 0.05). A linear support vector machine classifier yielded the best performance (79.75% accuracy, 67.74% sensitivity, 87.50% specificity). This work can serve to guide pre-processing in future lung cancer breath research and, more broadly, in infrared laser absorption spectroscopy in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".