Summary of Assessing Alcohol Use Disorder: Insights From Lifestyle, Background, and Family History with Machine Learning Techniques, by Chenlan Wang et al.
Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques
by Chenlan Wang, Gaojian Huang, Yue Luo
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates how factors like lifestyle, personal background, and family history impact the development of Alcohol Use Disorder (AUD). By analyzing survey data from 6,016 participants in the All of Us Program, researchers identified key determinants of AUD using decision trees. These include annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. The study also utilized data visualization and Chi-Square Tests of Independence to assess associations between these factors and AUD. To predict an individual’s likelihood of developing AUD, machine learning techniques like decision trees, random forests, and Naive Bayes were applied. Random forests achieved the highest accuracy (82%) compared to Decision Trees and Naive Bayes. The findings can offer insights for parents, healthcare professionals, and educators to develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how people’s lifestyles, backgrounds, and family histories affect their chances of developing a problem with alcohol (Alcohol Use Disorder or AUD). They analyzed surveys from 6,016 people in the All of Us Program. The researchers found some key things that can help predict if someone might develop AUD. These include things like how much money they make, what drugs they use, where they live, and their family history with AUD. They also used special tools to see which factors are connected to AUD. Then, they used machine learning (a type of computer program) to try to figure out who might develop AUD. The best method was a random forest, which got 82% correct! This can help us learn how to reduce the risk of AUD and get people the help they need early on. |
Keywords
» Artificial intelligence » Likelihood » Machine learning » Naive bayes » Random forest
