A Combined Predictive and Causal Approach for Neighborhood-Level Diabetes Detection
Bibliographic record
Abstract
Objective: Develop a neighborhood-level framework using machine learning and causal inference to identify socioeconomic and behavioral drivers of Type 2 diabetes for targeted public health interventions. Materials and Methods: Data from 1,149 Census Tracts in Toronto were integrated, linking demographic, health, and marginalization indices. Seven machine learning models classified neighborhoods with high diabetes prevalence. Feature engineering mitigated skewness and correlation, while Causal Forests estimated the Conditional Average Treatment Effect (CATE, τ) for predictors such as work stress, smoking, and mental health. Results: Predictive models achieved over 90% recall and high AUC metrics on both test and external validation datasets. Key predictors included obesity, overweight status, physical activity, and log-transformed median age. Causal analysis further indicated that elevated work stress (τ = 0.312) and daily smoking (τ = 0.155) increased diabetes risk, while stronger mental health (τ ≈ −1.1) was protective. Discussion: While genetic and clinical factors often dominate the conversation on diabetes, data is often restricted to confirmed diagnoses or not readily available for prevalence analyses. Our study shows how neighborhood contexts, including walkability, stress levels, and socioeconomic differences, help drive rising disease rates. We integrated machine learning classifiers with causal inference to examine how interventions, such as active transportation and adjusted work stress, could shift diabetes risk. Conclusion: This integrated method offers a blueprint for precision public health by clarifying how modifiable neighborhood factors affect diabetes risk. It can help tailor interventions to community needs and is applicable to other areas facing similar chronic disease challenges.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".