Breast Cancer Diagnosis using Quality Control Charts and Logistic Regression
Bibliographic record
Abstract
In this work, we propose data mining techniques for the diagnostic of breast cancer. The contribution of this work is data pre-processing which includes outliers removal and dimensionality reduction to visualize the data. After data visualization, the most appropriate machine learning algorithm can be proposed to perform this diagnosis. For the evaluation of our proposed approach, we have used UCI machine learning breast cancer Wisconsin (original) dataset (WBCD). We applied principal component analysis for dimensionality reduction in the pre-processing and different control charts and box plots for data visualization. After data visualization, WBCD is considered to be linearly separable and the logistic regression model is proposed for classification in the diagnosis. The evaluation of our model is performed using 10 fold cross validation with accuracy, recall, F1 score, confusion matrix and receiver operating characteristic (ROC) curves. In our proposed work, we have achieved 99.48% as accuracy, which is the highest result found on this dataset for 70-30% training-test partition.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".