A machine learning approach to predict e-cigarette use and dependence among Ontario youth
Bibliographic record
Abstract
INTRODUCTION: We developed separate random forest algorithms to predict e-cigarette (vaping) ever use and daily use among Ontario youth, and subsequently examined predictor importance and statistical interaction. METHODS: This cross-sectional study used a representative sample of Ontario elementary and high school students in 2019 (N = 6471). Vaping frequency over the last 12 months was used to define ever-vaping and daily vaping. We considered a large set of individual characteristics as potential correlates for ever-vaping (176 variables) and daily vaping (179 variables). Using cross-validation, we developed random forest algorithms and evaluated model performance based on the C-index, a measure to assess the discriminatory ability of a model, for both outcomes. Further, the top 10 correlates were identified by relative importance score calculation and their interaction with sociodemographic characteristics. RESULTS: There were 2064 (31.9%) ever-vapers, and 490 (7.6%) of the respondents were daily users. The random forest algorithms for both outcomes achieved high performance, with C-index over 0.90. The top 10 correlates of daily vaping included use of caffeine, cannabis and tobacco, source and type of e-cigarette and absence in last 20 school days. Those of ever-vaping included school size, use of alcohol, cannabis and tobacco; 9 of the top 10 ever-vaping correlates demonstrated interactions with ethnicity. CONCLUSION: Machine learning is a promising methodology for identifying the risks of ever-vaping and daily vaping. Furthermore, it enables the identification of important correlates and the assessment of complex intersections, which may inform future longitudinal studies to customize public health policies for targeted population subgroups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".