Predicting moderate drinking behaviors in National Health and Nutrition Examination Survey participants using biochemical and demographical factors with machine learning

Abstract

Recent studies revealed that any amount of alcohol consumption is an overall health detriment to multiple populations, contrary to popular beliefs. In addition, very few alcohol use studies utilized machine learning methods to compare the biological health of moderate drinkers compared to those that abstain from alcohol consumption, opting instead to focus on binge drinking and heavy drinking. Using participant data of multiple factor types from the National Health and Nutrition Examination Survey, we created prediction models with stacked ensembles and gradient boosting models. Machine learning models were used to identify which factors most enabled the prediction of moderate drinking behaviors. Our combined factor runs produced a cross-validation area under the curve (AUC) of 0.929 and a validation area under the curve of 0.806. Runs that only included biochemical or demographical factors received cross-validation AUC values of 0.825 and 0.925, and validation AUC values of 0.757 and 0.783, respectively. The top predictive factors for our machine learning runs, including gamma glutamyl transferase, gender, iron levels, and cigarette and marijuana usage, corroborate past studies that link those factors to alcohol consumption. Our findings identified key differences in the biological health of moderate drinkers compared to those that abstain from drinking. These results reveal a need to further explore the health effects of moderate drinking, especially for vulnerable populations.