Analysis for Predicting Respiratory Diseases from Air Quality Attributes Using Recurrent Neural Networks and Other Deep Learning Techniques
Bibliographic record
Abstract
The primary objective of this investigation is to establish a clear correlation between air quality and the prevalence of respiratory conditions, this is done by employing deep learning (DL) methodologies.RNN was compared against alternative methods such as GNN, CNN, feed-forward technique (DL), k-nearest neighbours, linear regression, decision tree, and neural network.Performance evaluations were conducted employing an Octuple crossvalidation approach, with the root mean square error (RMSE) employed to perform comparative analysis.RMSE serves as the primary metric for evaluating regression models, additional criteria for model comparison include computational efficiency, scalability, assessing model performance with larger datasets; and generalization to new data.While RMSE's simplicity and sensitivity to large errors are strengths, limitations include sensitivity to outliers and overlooking distributional differences.The data was collected on two major features, the first being the Air quality dataset which contained various gases present in the atmosphere that affect the air quality, and the second being the hospital patient dataset which indicates the number of people suffering from respiratory diseases due to the air quality.The study acknowledges the superior performance of the Recurrent Neural Network (RNN) model but suggests the need for new learning methods handling small datasets and explores efficient, scalable approaches like distributed and federated learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".