An Improved CHI2 Feature Selection Based a Two-Stage Prediction of Comorbid Cancer Patient Survivability
Bibliographic record
Abstract
There are theoretical and practical ramifications to modelling cancer patients' survival with concurrent illnesses.Cancer is one of the leading causes of mortality worldwide.Stomach, liver, thyroid, lungs, and skin cancers are a few of the more common types.The early identification and prevention of these malignancies are important goals.Recent investigations have found that some patients suffer cancer-related co-morbidities.Studies show that comorbid conditions worsen the prognosis of cancer patients.There are several methods that might have led to this finding.With hazard ratios ranging from 1.1 to 5.8, the majority of studies discovered that cancer patients with comorbidity had a poorer 5-year survival rate than those without.Just a few research have examined the effects of certain chronic conditions.There is no proof that comorbidity causes more aggressive cancers.Our research indicates that forecasting survival is a two-stage issue.Predicting a patient's fiveyear survival rate is the initial step.In the second phase, those whose expected outcome is "death" are told how long they have left to live.Male and female concurrent cancer cases were identified and categorised using the SEER database (Stomach, Lung, Liver, Thyroid and Skin Cancers).The dataset was handled throughout the classification phase using CHI 2based feature selection.These two techniques addressed the issues that an inconsistent data set raised.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".