Analysis of Machine Learning Methods for COVID-19 Detection Using Serum Raman Spectroscopy
Bibliographic record
Abstract
One of the most challenging aspects of the emergent coronavirus disease 2019 (COVID-19) pandemic caused by infection of severe acute respiratory syndrome coronavirus 2 has been the need for massive diagnostic tests to detect and track infection rates at the population level. Current tests such as reverse transcription-polymerase chain reaction can be low-throughput and labor intensive. An ultra-fast and accurate mode of detecting COVID-19 infection is crucial for healthcare workers to make informed decisions in fast-paced clinical settings. The high-dimensional, feature-rich components of Raman spectra and validated predictive power for identifying human disease, cancer, as well as bacterial and viral infections pose the potential to train a supervised classification machine learning algorithm on Raman spectra of patient serum samples to detect COVID-19 infection. We developed a novel stacked subsemble classifier model coupled with an iteratively validated and automated feature selection and engineering workflow to predict COVID-19 infection status from Raman spectra of 250 human serum samples, with a 10-fold cross-validated classification accuracy of 98.0% (98.6% precision and 98.5% recall). Furthermore, we benchmarked nine machine learning and artificial neural network models when evaluated using eight standalone performance metrics to assess whether ensemble methods offered any improvement from baseline machine learning models. Using a rank-normalized scores derived from the performance metrics, the stacked subsemble model ranked higher than the Multi-layer Perceptron, which in turn ranked higher than the eight other machine learning models. This study serves as a proof of concept that stacked ensemble machine learning models are a powerful predictive tool for COVID-19 diagnostics.
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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 | 0.005 | 0.009 |
| 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".