Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population
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Bibliographic record
Abstract
Background: This study aimed to identify some risk factors associated with time to diabetes type II events using artificial intelligence (AI) survival models (SM) in a population cohort from East Azerbaijan, Iran. Methods: Data from Azar-Cohort spanning from 2014 to 2020 was analyzed using the random forest (RF) variable selection method along with Cox regression to identify the most relevant risk factors associated with diabetes. We then developed prediction models using RF survival analysis. Lasso-variable selection and RF variable selection were used to select the most important variables. The concordance index (C-index) was used to evaluate the concordance of the prediction models. Results: Our LASSO-Cox regression identified six factors to be significantly associated with diabetes: age, mean corpuscular hemoglobin concentration (MCHC), waist circumference (WC), body mass index (BMI), use of sleep medication, and hypertension stage 1 and stage 2. The model included all variables with a C-index of 76.3%. In contrast, the RF analysis identified 21 important variables predicting a higher probability of having diabetes. Of those, WC, MCHC, triglyceride, and age were the most important predictors of diabetes. The RF model converged after 500 trees with an out-of-bag (OOB) of 0.28 and a C-index of 79.5%. Conclusion: RF machine learning algorithms and LASSO-Cox regression analyses consistently identified WC, hypertension, and MCHC as the main risk factors for developing diabetes. The RF approach demonstrated slightly better accuracy in predicting the likelihood of diabetes at different time points.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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