用机器学习分析生活习惯与家族史，预测酒精使用障碍风险。
Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques
- 基于6016人数据，用决策树识别收入、药物使用等关键风险因素。
- 随机森林模型预测准确率达82%，优于其他算法。
- 结果有助于家庭和医生早期干预，适合公共卫生研究者参考。
本研究探讨了生活方式、个人背景及家族史对酒精使用障碍（AUD）风险的影响。利用来自All of Us项目的数据，提取了6,016名参与者关于AUD状态、生活方式、个人背景及家族史的信息。通过决策树识别出年收入、娱乐性药物使用、居住年限、性别/性别、婚姻状况、教育水平和家族史等关键决定因素。随后采用数据可视化和卡方独立性检验评估这些因素与AUD的关联。接着应用决策树、随机森林和朴素贝叶斯等机器学习技术预测个体发展AUD的可能性。结果显示，随机森林模型准确率最高，达82%，优于决策树和朴素贝叶斯。研究结果可为家长、医疗专业人员和教育工作者制定降低AUD风险的策略提供依据，支持早期干预与针对性预防措施。
原文摘要 · Abstract (English)
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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