Huang Xinya, Dai Zheng, Wang Kesheng, Luo Xingguang
School of Computer, North China University of Technology, Shijingshan District Beijing, P.R. China 100144; Brunel University, London UB8 3PH, UK.
Health Affairs Institute, Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA.
Proc 2024 9th Int Conf Math Artif Intell (2024). 2024 May;2024:1-10. doi: 10.1145/3670085.3670090. Epub 2024 Aug 22.
Little is known about the association of social media and belief in alcohol and cancer with binge drinking. This study aimed to perform feature selection and develop machine learning (ML) tools to predict occurrence of binge drinking among adults in the United State. A total of 5,886 adults including 1,252 who ever experienced with binge drinking were selected from the 2022 Health Information National Trends Survey (HINTS 6). Feature selection of 69 variables was conducted using Boruta and the Least Absolute Shrinkage and Selection Operator (LASSO). The Random Over Sampling Example (ROSE) method was utilized to deal with the imbalance data. Seven machine learning (ML) tools including the Support Vector Machines (SVMs) algorithms, Logistic Regression, Naïve Bayes, Random Forest, K-Nearest Neighbor, Gradient Boosting Machine, and XGBoost were applied to develop ML models to predict binge drinking. The overall prevalence of binge drinking among U.S. adults is 21.3%. Both Boruta and LASSO selected 28 identical variables. SVM with Radial Basis Function revealed the best model with the highest accuracy of 0.949 and sensitivity of 0.958. The top risk factors of binge drinking were tobacco use (e-cigarette use and smoking status), belief in alcohol (alcohol decreases the risk of future health), belief in cancer (prevention is not possible, worry about getting cancer), and social media (social media visits and sharing health information). These findings underscore the need for multiple health behavior interventions to enhance education related to alcohol use and cancer and how to effectively employ social media to improve health outcomes.
关于社交媒体以及饮酒与癌症的信念和暴饮之间的关联，人们了解甚少。本研究旨在进行特征选择并开发机器学习（ML）工具，以预测美国成年人中暴饮的发生情况。从2022年健康信息国家趋势调查（HINTS 6）中选取了总共5886名成年人，其中包括1252名曾有过暴饮经历的人。使用Boruta和最小绝对收缩与选择算子（LASSO）对69个变量进行特征选择。采用随机过采样示例（ROSE）方法处理不平衡数据。应用包括支持向量机（SVM）算法、逻辑回归、朴素贝叶斯、随机森林、K近邻、梯度提升机和XGBoost在内的七种机器学习（ML）工具来开发预测暴饮的ML模型。美国成年人中暴饮的总体患病率为21.3%。Boruta和LASSO都选择了28个相同的变量。具有径向基函数的支持向量机显示出最佳模型，准确率最高为0.949，灵敏度为0.958。暴饮的首要风险因素包括烟草使用（电子烟使用和吸烟状况）、饮酒信念（饮酒可降低未来健康风险）、癌症信念（无法预防、担心患癌）以及社交媒体（社交媒体访问和分享健康信息）。这些发现强调了需要采取多种健康行为干预措施，以加强与饮酒和癌症相关的教育，以及如何有效利用社交媒体来改善健康状况。
