Enhanced Diagnosis of the COVID-19 Behaviour Using the Rough Set Theory and Genetic Algorithms
Bibliographic record
Abstract
Abstract The outbreak of the coronavirus 2019 (COVID-19) has created an excellent challenge for the care system worldwide. One in every of the foremost vital points of this challenge is that the management of COVID-19 patients needing acute and/or vital metastasis care. The main objective of applying data mining to Covid-19 dataset is essential to propel learning by empowering data-oriented decision making to improve existing clinical practices and learning materials. Current data mining techniques offer patient data analysis for achieving an automated diagnosis of the diseases as an example; however, the results are not very accurate nor reliable, especially with a dynamic virus as the COVID-19. In this paper, we are proposing a multi-stage diagnostic (MSD-Covid19) model to enhance the diagnosis of the COVID-19, and to provide a sustainable automated system to improve the healthcare systems and patient outcomes. The first stage includes a selection of a classification model with no reduction attributes. Tested classification algorithms include Deep learning, Multilayer Perceptron, KNN, Bayesian Auto Regression, Logistic Model Trees (LMT), Hoeffding tree (VFDT), and Fuzzy Unordered Rule Induction Algorithm. In the second stage, a rough set reduction algorithm based on genetic algorithms is employed, and finally, an optimization of the classification is conducted using the reduced attributes. The proposed model is evaluated on a global COVID-19 dataset. Experimental results demonstrate that the proposed MSD-Covid19 has a great contribution to increase the diagnostic accuracy of the COVID-19 disease behaviour.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".