Enhanced Insights into Alcohol Use Disorder from Lifestyle, Background, and Family History in a Large-Scale Machine Learning Study

Alcohol Use Disorder (AUD) is a multifactorial condition with severe individual and societal impacts. Ex- tending our 2024 study, this work examines lifestyle, background, and family history determinants of AUD using an expanded dataset from the All of Us Research Program. The updated analysis includes approxi- mately 2.5 times more participants than the prior study, enabling improved statistical power and evaluation of result stability over time. Using interpretable machine learning models and statistical analyses, we identified annual income, residential stability, recreational drug use, sex/gender, marital status, education, and family history as key contributors to AUD risk. Annual income remained the most influential predictor across both datasets, while other feature rankings showed modest shifts. Family history factors continued to demonstrate non-linear effects, with close relatives’ AUD status remaining influential despite differences between statisti- cal association and predictive importance. In predicting AUD versus non-AUD status, Random forest models achieved the highest classification accuracy (81%), consistent with 2024 results but with improved precision for identifying AUD cases. Overall, the findings confirm the robustness of previously identified AUD de- terminants and underscore the need for coordinated, multi-level prevention strategies addressing behavioral, familial, and structural factors contributing to AUD.