Bivariate Beta Regression Model and Its Medical Applications
Bibliographic record
Abstract
Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. Among all statistical frameworks, regression has been proven to be one of the most potent tools in prediction. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is essential. Thus, a model must be selected to fit the data well, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. In this work, a bivariate Beta regression model has been proposed, which is based on a flexible bivariate Beta distribution with three shape parameters. Then, the performance of this algorithm is evaluated in terms of the accuracy of the prediction and compared to a similar regression model. The accuracy of model performance depends on the nature and complexity of the dataset. The results in this paper show the merits of our work.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".