A Study to Evaluate Various Machine Learning Approaches for Breast Cancer Prediction and Detection
Bibliographic record
Abstract
Breast cancer, prevalent among women, especially those with a genetic predisposition, presents substantial challenges in understanding and mitigating its impacts. Early detection is paramount to prevent the progression and spread of this disease. This study evaluates ten different machine learning algorithms: Logistic regression, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Autoencoder, Hybrid, Gaussian Mixture Model, Naïve Bayes, and Gradient Boosting, to understand their efficacy and adaptability across various datasets. Our exploration provides insights into the proficiency of these algorithms in breast cancer estimation and early prediction. Naive Bayes, Gradient Boosting, and Random Forest emerged as the superior performers, demonstrating accuracies of 97.37%, 80.36%, and 92.31%, respectively, across three different datasets. Each algorithm was evaluated on three different datasets, thereby offering a comprehensive understanding of their applicability in distinct breast cancer detection environments.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".