Finite Multivariate McDonald's Beta Mixture Model Learning Approach in Medical Applications
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Bibliographic record
Abstract
In this paper, we present a finite mixture model with a generalization of Beta distribution called the McDonald's Beta Mixture Model (McBMM). The parameters of the McBMM are estimated via the maximum likelihood estimation technique and EM algorithm using the Newton-Raphson technique as an iterative approach that assists in computing the updated parameters. We apply our proposed model to medical applications namely, targeting treatment for heart disease patients based on clinical data, breast tissue analysis considering histopathological images, and malaria detection using histological images. Compared to the Gaussian mixture model (GMM), the Beta mixture model performs better on data with strictly bounded values and asymmetric distribution. Three real-world datasets are modelled using the new McBMM, showing that this model fits better than the GMM and has better accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it