Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques
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 participa...
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| Published in | Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare Vol. 14; no. 1; pp. 16 - 20 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Los Angeles, CA
SAGE Publications
01.09.2025
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| Online Access | Get full text |
| ISSN | 2327-8595 2327-8595 |
| DOI | 10.1177/2327857925141005 |
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| 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. |
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| AbstractList | 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. |
| Author | Wang, Chenlan Huang, Gaojian Luo, Yue |
| Author_xml | – sequence: 1 givenname: Chenlan surname: Wang fullname: Wang, Chenlan organization: University of Michigan, Ann Arbor – sequence: 2 givenname: Gaojian surname: Huang fullname: Huang, Gaojian organization: San José State University, San Jose – sequence: 3 givenname: Yue surname: Luo fullname: Luo, Yue organization: San José State University, San Jose |
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