A REVIEW ON DIFFERENT MACHINE LEARNING ALGORITHMS TO BREAST CANCER RISK PREDICTION
Bibliographic record
Abstract
Breast cancer is the second most fatal kind of cancer in women, accounting for almost a quarter of all cancer deaths. About 10 percent of women worldwide are diagnosed with breast cancer at some time in their lives, making it one of the most frequent malignancies among women today. However, although the treatment for this cancer is now accessible in practically all first world and some third world countries, the primary problem occurs when the cancer is not appropriately detected at the very beginning of the disease's progression. Several researchers have made significant contributions to early diagnosis, improved prognosis, and better treatment of BC during the previous two decades, resulting in a decrease in the death rate. Machine Learning has shown to be quite useful in this discipline, particularly in the prediction of illnesses such as cancer. So far, classification and data mining approaches have shown to be dependable and successful means of categorizing data. These strategies have been used to forecast and make judgments in a variety of fields, particularly the medical industry. Throughout this study, we examined the current state of the art in BC prediction, which included breast cancer diagnosis, BC risk prediction using several machine learning algorithms, and breast cancer recurrence prediction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".