Feature Selection Algorithm using Fuzzy Rough Sets for Predicting Cervical Cancer Risks
Bibliographic record
Abstract
Early detection or prediction is very important to reduce the fatalities of Cervical Cancer. Cancer cells affect the Cervix area initially, and then it will spread near by parts. A method using Fuzzy Rough sets is used to analyze the demographic dataset and identify the risk of Cervical Cancer. This method integrates Entropy, Information Gain (IG) and Fuzzy Rough sets for identifying the risk of Cervical Cancer earlier. Risk Factors are identified by IG. Rules are extracted by Fuzzy Rough sets. These rules can be used to identify the risk of Cervical Cancer efficiently that the decision trees. It is found that Human Papilloma Virus (HPV) and having Multiple Sexual Partners (MP) are the major risk factors increase the chances of affecting this cancer. If all the above factors are high the risk of affecting Cervical Cancer is high. Result of this paper will help to improve the clinical practice guidance for analyzing the risk of Cervical Cancer.Keywords: Cervical Cancer, Entropy, Information Gain, Fuzzy Rough Sets, Demographic Data, Feature selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".