Recurrent neural network-based automated early detection of pandemic-prone diseases through symptoms analysis
Bibliographic record
Abstract
A pandemic is a disease outbreak that affects an alarmingly high percentage of the population and spreads over a large geographic area. Some diseases are more likely to start pandemics because they are contagious and can cause severe illness or death. The most recent lethal disease to emerge and cause global devastation among all other pandemics is COVID-19. Since the COVID-19 disease breakout in December 2019, it has become a global pandemic that has caused millions of people throughout the planet. Modern information technology applications have emerged as a result of the pandemic’s heightened demand for healthcare products and services. To effectively manage future pandemic-prone diseases and slow their vigorous spread, their early detection is crucial. New-age technologies, such as artificial intelligence, internet of things, cloud computing, etc., are therefore a major asset in the fight against such terrible diseases. Therefore, the current study employs the proposed architecture of a recurrent neural network (RNN) for the accurate identification of patients infected with COVID-19 disease through analysis of its major symptoms. Before classification, extra trees-based feature selection was used to collect the most significant features that accurately characterize COVID-19 positive or COVID-19 negative classes. This work also takes into account several deep learning models viz., RNN, Bi-LSTM, GRU, LSTM along with other machine learning models such as Logistic Regression to identify the COVID-19 pandemic. The thorough analysis of results shows that, the RNN classifier appears to perform better than the other classifiers in the early diagnosis of this fatal disease, with an accuracy of 98.70%, sensitivity of 97.81%, specificity of 95.55%, precision value equal to 98.97%, F1-score of 96.16%, and false discovery rate of 1.03% only. Therefore, the proposed RNN-based approach can help identify fatal infections caused by pandemic-prone diseases at an early stage, enabling timely intervention and containment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".