COVID-19 detection from optimized features of breathing audio signals using explainable ensemble machine learning
Bibliographic record
Abstract
The automatic detection of COVID-19 using smartphone-recorded breathing signals in a ubiquitous and non-invasive way holds great promise. However, achieving accurate detection is challenging due to breathing signals' noisy and non-stationary nature, lack of distinguishable features, and imbalanced COVID/non-COVID data scenarios. This paper proposes an explainable ensemble learning-based framework for COVID-19 detection that extracts features from breathing signals through multiresolution analysis. First, we extract 165-dimensional features from the decomposed coefficients of a two-level discrete wavelet transformed (DWT) signal. From these, 27 optimized features are selected using the Recursive Feature Elimination with Cross-Validation (RFECV) technique. The level-2 DWT decomposed approximation coefficients retain frequencies in the 0–150 Hz range, aligning with human breathing frequencies. We utilize an ensemble model comprising decision trees, random forests, gradient boost, and XGBoost classifiers with a majority voting strategy for the detection task. A balanced and augmented dataset is prepared using the publicly available Coswara dataset. The results show that the ensemble approach improves accuracy compared to the individual models. Further, we explore the model's interpretability using Shapley additive explanations values, finding that the model places primary importance on features such as the RMS value, higher pitch of short-time Fourier transform, and higher frequency components of the Mel spectrogram, which align well with known COVID-related breathing characteristics. A comparison with related works demonstrates the effectiveness of our proposed feature extraction and ensemble framework, achieving an accuracy of 97.5 % and specificity of 95.24 %. These findings can potentially support smartphone-based COVID-19 detection applications using breathing signals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 0.000 |
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".