Predicting the stage of gastric cancer after gastrectomy based on machine learning algorithms
Bibliographic record
Abstract
Abstract Background Gastric cancer (GC) is the fourth most common cause of cancer death worldwide, with a 5-year survival rate of less than 40%. One of the most important methods for diagnosing stomach cancer is endoscopy, which is quite costly and invasive. The aim of this study was to develop machine learning-based diagnostic prediction models for the stage of GC. Objectives To create a highly accurate predictive model for the stage of GC in patients via a noninvasive method based on machine learning (ML). Methods In this study, data from 996 patients with GC after gastrectomy were utilized. The data were split into groups, trained and tested, and a ratio of 8:2 was used to develop different machine learning models. Furthermore, the six different machine learning algorithms used in predicting the stage of GC include decision tree (DT), K nearest neighbor (KNN), logistic regression (LR), naive Bayes (NB), random forest (RF), and support vector machine (SVM) methods. Results: The analysis of the demographic variables revealed statistically significant differences in the PLR and NLR and other parameters between the two groups of patients with stages I and III gastric cancer (P < 0.05). Results The analysis of demographic variables revealed statistically significant differences in the PLR, NLR, and other variables between the two groups of patients with stages I and III gastric cancer, with a significance level of P-value < 0.05. Moreover, these findings suggest that the KNN model in this study is one of the best models for predicting the stage of GC.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".