Machine Learning Algorithms to Predict Heavy Episodic Drinking in the United States Using Survey Data
Bibliographic record
Abstract
INTRODUCTION: Heavy episodic drinking (HED) is a major public health concern but is often missing from surveys or measured unreliably. Predictive models offer a method to estimate HED's likelihood at the individual level in such cases. While logistic regression is commonly used, other machine learning algorithms (MLA) may offer greater accuracy and robustness. This study compares various MLAs to identify the best predictive model of HED. METHODS: Data from the 1997-2018 National Health Interview Survey were used. Six MLAs were trained and cross-validated: logistic regression, naïve bayes, k-nearest neighbour, support vector machine, random forest and XGBoost. Model performance was compared, and the SHapley Additive exPlanations (SHAP) method assessed interpretability by ranking features based on their contribution to the model's prediction. RESULTS: The probability of correctly ranking a randomly selected HED instance higher than a non-HED instance ranged from 0.85 to 0.97 (with values closer to 1 indicating better performance). XGBoost outperformed the other MLAs (sensitivity 0.80, precision 0.83, accuracy 0.92). Amongst the 11 features included in the models, average daily alcohol use and age were the most influential, as determined by SHAP values. DISCUSSION AND CONCLUSIONS: The strong discriminative ability of our models shows that even a limited number of well-chosen features can yield robust predictions, highlighting the potential of MLAs for modelling health behaviours. Integrating our models into simulation frameworks can help model HED and test scenarios, leading to effective policies. Future studies should incorporate objective sources for external validation and investigate systematic biases to improve predictive accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".