使用惩罚回归和机器学习来建模烟草依赖:情绪,习惯和戒烟意图的预测作用
Arzu Bulut1, Gökhan Aba1, Sinem Kabak1
1Bandirma Onyedi Eylul University, Balikesir, Türkiye.
Journal of ethnicity in substance abuse
|January 13, 2026
概括
使用机器学习和惩罚性回归来预测大学生的尼古丁依赖,揭示了关键的心理社会和行为因素. 拉索模型确定了情绪症状,吸烟习惯和戒烟意图作为重要的预测因素.
科学领域:
- 心理学 心理学 心理学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 尼古丁依赖是大学生中一个重要的公共卫生问题.
- 了解尼古丁依赖的预测因素对于开发有效的干预措施至关重要.
- 以前的研究主要集中在生理因素上,不太重视心理社会和行为决定因素.
研究的目的:
- 在土耳其大学生中预测尼古丁依赖水平 (尼古丁依赖法格斯特罗姆测试 - FTND分数).
- 为了比较惩罚回归和机器学习模型在预测尼古丁依赖方面的表现.
- 确定尼古丁依赖的重要心理社会,行为和社会人口学预测因素.
主要方法:
- 一项涉及土耳其1,120名大学生的横截面研究.
- 利用了24个心理,行为,动机和社会人口统计学变量.
- 我们比较了六种预测模型:线性回归,LASSO,Ridge,弹性网,SVR和XGBoost.
主要成果:
- 处罚回归模型和调整的XGBoost显示了可比的预测性能.
- XGBoost实现了最高的准确性 (R2 = 0.621),而LASSO提供了卓越的解释性.
- 拉索模型确定了11个重要的预测因素,解释了59%的FTND得分差异,包括情绪症状,吸烟习惯和戒烟意图.
结论:
- 像LASSO这样的可解释模型对于识别尼古丁依赖的可修改风险因素非常有价值.
- 尼古丁依赖受到心理社会,行为和人口因素的复杂相互作用的影响,这些因素超越了生理方面的影响.
- 研究结果为有针对性的干预措施提供了见解,以减少大学生的尼古丁依赖.
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