Predicting Depression in Canadians with or at Risk of Diabetes: A Cross-Sectional Machine Learning Analysis
Bibliographic record
Abstract
Abstract Depression often goes unrecognized in individuals at risk or living with diabetes, presenting considerable challenges for primary care clinicians. Although large language models and other foundation model approaches are drawing significant attention, we systematically compared six established machine learning algorithms-Logistic Regression, Random Forest, AdaBoost, XGBoost, Naive Bayes, and Artificial Neural Networks-chosen for their reliability, interpretability, and feasibility in everyday clinical settings. By benchmarking their performance under real-world constraints, we identified key factors linked to depression risk in diabetes care, including patient sex, age, osteoarthritis, hemoglobin A1c, and body mass index. Although incomplete demographic information and potential label bias limited predictive power, our results demonstrate that a diverse set of clinical features can still help pinpoint high-risk patients. They also indicate a need for longitudinal follow-up and richer clinical data to enhance model accuracy. As a practical benchmark for both clinicians and data scientists, this work suggests that machine learning–based risk stratification can improve early detection of depression and inform targeted interventions in diabetic populations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".