Machine Learning-based Colorectal Cancer Prediction using Global Dietary Data
Bibliographic record
Abstract
Abstract Background Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Active screening for CRC yields detection in increasingly younger cohorts. However, current machine learning algorithms that are trained using older adults and smaller datasets, may not perform well in practice for large populations. Aim To evaluate machine learning algorithms using large datasets accounting for both younger and older adults from multiple regions and diverse sociodemographic. Methods Dietary-related colorectal cancer data was derived for Canada, India, Italy, South Korea, Mexico, Sweden, and United States from the Center for Disease Control and Prevention, Global Dietary database, and other publicly accessible institutional sites. Nine supervised and unsupervised machine learning algorithms were evaluated. Results 109,342 data points were used, of which 7,326 had positive CRC labels. Both supervised and unsupervised models performed well in predicting CRC and non-CRC labels. An artificial neural network (ANN) was found to be the optimal algorithm with CRC misclassification of 1% and non-CRC misclassification of 3%. Conclusions ANN models trained on large heterogeneous datasets may be applicable for both younger and older adults. Such models represent effective clinical decision support systems assisting healthcare providers in dietary-related, non-invasive screening that can be applied in large populations. Using optimal algorithms coupled with high compliance to cancer screening is expected to significantly improve early diagnoses and boost the success rate of timely and appropriate cancer interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".