Bibliographic record
Abstract
Since diabetes is prone to serious complications, the construction of a mathematical model of diabetes plays an important role in the diagnosis and prevention of diabetes. In this paper, 769 female cases of diabetes were found, including the number of pregnancies, glucose, insulin, body mass index, genetic function of diabetes, age and so on. They were separated into training set and test set by the ratio of 8:2. Logistic regression (LR) and support vector machine (SVM) were used to classify and identify female diabetic patients. The results showed that the RBF SVM method is superior to LR in terms of accuracy, and F1score. But the Ridge regression is better in the evaluation of recall. Then the fusion model combined of Ridge method and RBF SVM method could improve the result in the evaluation of diagnosis of female diabetes. The accuracy, recall and F1score showed the fusion model is more suitable for the identification and diagnosis of female diabetic patients. The research in this paper helps doctors diagnose diabetes quickly and has guiding significance for clinical treatment-related chronic diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".