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

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 rankin

SUBMITTER: Llamosas-Falcon L 

PROVIDER: S-EPMC12682420 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature