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Machine Learning Algorithms to Predict Heavy Episodic Drinking in the United States Using Survey Data.

Laura Llamosas-Falcón^1,2, Charlotte Probst^1,2,3,4, Kevin Shield^1,3,5

¹Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Canada.

Drug and Alcohol Review

|November 5, 2025

Summary

Machine learning algorithms accurately predict heavy episodic drinking (HED) using survey data. XGBoost demonstrated superior performance, identifying average daily alcohol use and age as key predictors for public health policy.

Keywords:

SHAP alcohol use heavy episodic drinking machine learning public health

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Area of Science:

Public Health
Data Science
Health Informatics

Background:

Heavy episodic drinking (HED) presents a significant public health challenge, often underestimated due to survey limitations.
Predictive modeling offers a solution for estimating individual HED risk when direct measurement is unreliable.
Machine learning algorithms (MLAs) may surpass traditional logistic regression in predicting HED.

Purpose of the Study:

To compare the predictive performance of various MLAs for identifying heavy episodic drinking.
To determine the most accurate and robust MLA for HED prediction.
To assess feature importance for HED prediction using SHAP values.

Main Methods:

Utilized data from the National Health Interview Survey (1997-2018).
Trained and cross-validated six MLAs: logistic regression, naïve bayes, k-nearest neighbour, support vector machine, random forest, and XGBoost.
Employed the SHapley Additive exPlanations (SHAP) method for interpretability and feature ranking.

Main Results:

XGBoost exhibited the highest performance (accuracy 0.92, sensitivity 0.80, precision 0.83).
Model performance, measured by the probability of correctly ranking HED instances, ranged from 0.85 to 0.97.
Average daily alcohol consumption and age were identified as the most significant predictors of HED via SHAP analysis.

Conclusions:

Selected features can yield robust HED predictions, demonstrating MLA potential for health behavior modeling.
Integrating these models into simulation frameworks can inform effective public health policies for HED.
Future research should focus on external validation and bias investigation for enhanced predictive accuracy.