Dynamic Feature Engineering for Breast Cancer Risk Stratification: A Machine Learning System Integrating Clinical Guidelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast cancer is one of the most common malignancies among women worldwide, posing a major public health challenge due to its high incidence and complex biological characteristics. Breast cancer screening is the cornerstone of tumor prevention and requires the systematic integration of morphological biomarkers and clinical guidelines. This study proposes a dynamic feature engineering framework, which encodes tumor biology through nonlinear transformations, including the square root transformation of tumor radius to simulate the growth of cubic volume ( , The risk decays along with age stratification. When evaluated on the Wisconsin Diagnostic Breast Cancer Dataset (WDBC), XGBoost performed very well in terms of clinical information characteristics, with an AUC of 0.90 (sensitivity =92%, specificity =88%), outperforming the 7.2% of the linear model. This transformation effectively linearizes the cubic relationship between tumor radius and volume. These results emphasize that combining algorithm design with oncological principles can enhance predictive accuracy while reducing unnecessary interventions, providing a blueprint for AI-driven precision oncology.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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