Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques

TL;DR

Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants, and machine learning techniques were applied to predict an individual’s likelihood of developing AUD.

Abstract

This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision trees including annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. Data visualization and Chi-Square Tests of Independence were then used to assess associations between identified factors and AUD. Afterwards, machine learning techniques including decision trees, random forests, and Naive Bayes were applied to predict an individual's likelihood of developing AUD. Random forests were found to achieve the highest accuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this study can offer insights that help parents, healthcare professionals, and educators develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts.

Figures (9)

• Figure 1: The importance of each factor from the three surveys (i.e., features) in relation to whether the individual has AUD.
• Figure 2: The importance of whether other family members have AUD in relation to the individual's AUD status.
• Figure 3: At each annual income level, the ratio of participants with AUD to the total number of participants at that income level is displayed.
• Figure 4: Given the duration of residence at the current address, the ratio of participants with AUD to the total number of participants sharing the same length of residence.
• Figure 5: For each type of recreational drug, the ratio of participants with AUD to the total number of participants who have used that drug is presented.