Research and Model Development for Gastric Cancer Risk Prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gastric cancer stands out as one of the most widespread deadly cancers which produces substantial rates of sickness and death throughout the world. The ability to predict gastric cancer risk early is vital to enhance patient recovery and survival statistics. The study integrated clinical and lifestyle data from public databases and simulated data which underwent preprocessing through missing value imputation and feature engineering steps including BMI creation, dietary score calculations and age grouping in combination with data balancing techniques including the Synthetic Minority Oversampling Technique (SMOTE). Three predictive machine learning models including Random Forest, Logistic Regression, and Extreme Gradient Boosting (XGBoost) underwent development and evaluation based on accuracy, precision, recall, F1-score, and Area Under the ROC Curve (AUC). The XGBoost model delivered superior performance based on experimental results achieving top scores in accuracy (0.8592), precision (0.8541), recall (0.8592), and F1-score (0.8555) which demonstrated its strong predictive power. The Logistic Regression model achieved the top AUC score of 0.8066 which demonstrates its superior ability to interpret probabilities. The study demonstrates how machine learning and specifically the XGBoost model can accurately forecast gastric cancer risk which helps enable timely medical interventions and supports tailored treatment strategies.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it