在彩票忠诚度计划成员中评估问题博风险:机器学习方法
1University of Maryland, 525 West Redwood Street, Baltimore, MD 21201, United States.
Addictive behaviors
|May 14, 2025
概括
彩票忠诚度计划可以帮助识别博问题,但随机森林机器学习模型的灵敏度不足以有效地检测有风险的个人. 需要进行进一步的研究,以改善博问题的查方法.
科学领域:
- 行为科学 行为科学
- 机器学习应用 机器学习应用
- 公共卫生 公共卫生
背景情况:
- 彩票博通常被认为是低风险的,但有博问题的个人可能只玩彩票.
- 彩票忠诚度计划收集有价值的玩家数据 (人口统计数据,购买历史),可以用于博问题选.
- 问题博的流行可能在特定人群中更高,例如忠诚度计划参与者.
研究的目的:
- 评估使用机器学习算法在州彩票忠诚度计划中识别博问题个人的可行性.
- 用忠诚度计划数据评估随机森林分析在预测问题博方面的有效性.
- 确定彩票忠诚度计划是否是早期发现和预防与博相关的伤害的可行环境.
主要方法:
- 分析了来自5903名忠诚度计划参与者的数据 (机票上传与在线调查合并).
- 问题博严重性指数 (PGSI) 选了问题博 (得分≥5).
- 采用随机森林分析,根据调查数据预测问题博,包括博频率和支出.
主要成果:
- 在接受调查的忠诚度计划参与者中,有14% (n = 809) 的人发现有博问题,比一般人口样本更高的患病率.
- 随机森林模型的整体性能中等,但灵敏度较差,无法有效识别有博问题的个体.
- 虽然彩票忠诚度计划由于患病率较高而显示出选的潜力,但随机森林方法对于风险检测不是最佳的.
结论:
- 彩票忠诚度计划为选和二次预防问题博提供了一个有希望的途径,原因是患病率较高.
- 随机森林算法虽然是一种强大的预测技术,但在此背景下检测问题博的灵敏度有所限制.
- 替代机器学习方法或修改可能是必要的,以提高在忠诚度计划中的问题博选工具的准确性和有效性.
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