与戒烟服务相关的因素 在线自我查问卷的用户中要求戒烟服务
Norberto F Hernández-Llanes1,2, Ricardo Sánchez-Domínguez2, Sofía Álvarez-Reza2
1Equipo de Ciencia de Datos, Centros de Integración Juvenil AC, Ciudad de México, México.
Substance use & misuse
|December 28, 2024
概括
机器学习有效地预测了谁在墨西哥寻求在线戒烟服务. 关键因素包括年龄,性别和依赖程度,有助于有针对性的公共卫生运动.
科学领域:
- 公共卫生 公共卫生
- 数字健康数字健康
- 机器学习 机器学习
背景情况:
- 烟草吸烟是全球卫生危机,墨西哥面临着独特的挑战,无法获得戒烟服务.
- 基于互联网的戒烟 (I-BC) 提供了一个可扩展的解决方案,机器学习 (ML) 可以预测治疗参与度.
- 了解I-BC请求的预测因素对于优化墨西哥公共卫生干预至关重要.
研究的目的:
- 利用机器学习 (ML) 来识别与请求基于互联网的终止 (I-BC) 服务相关的特征.
- 分析墨西哥尼古丁依赖的在线自我评估问卷数据.
- 开发一个预测模型来识别可能寻求戒烟支持的个人.
主要方法:
- 追溯分析了14182个18岁以上的个人记录,他们完成了在线尼古丁依赖查.
- 将随机森林算法与四种过量采样方法进行比较,以确定最佳预测模型.
- 测量模型中预测变量的相对重要性.
主要成果:
- 随机森林模型实现了78.6%的灵敏度和68.8%的特异性.
- 要求戒烟服务的关键预测因素包括年龄,性别,尼古丁依赖程度,特定的墨西哥州 (例如墨西哥,西纳罗亚州) 以及世界无烟日等活动.
- 该研究确定了影响I-BC吸收的显著的人口和上下文因素.
结论:
- 随机森林算法有效地预测了可能使用戒烟服务的个人.
- 识别的预测变量为设计有针对性的预防和宣传活动提供了宝贵的见解.
- 这些发现可以提高活动的有效性,并增加获得戒烟支持的机会.
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