Dynamic Feature Engineering for Breast Cancer Risk Stratification: A Machine Learning System Integrating Clinical Guidelines
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".