机器学习算法的适用性,以预测巴西吸烟者的治疗干预成功
Miyoko Massago1, Mamoru Massago2, Pedro Henrique Iora3
1PhD Student in the Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil.
PloS one
|March 4, 2024
概括
机器学习模型可以预测戒烟的成功. 戒烟药物使用和更少的复发改善了结果,而更高的香烟消费阻碍了戒烟.
科学领域:
- 公共卫生 公共卫生
- 计算医学是一种计算医学.
- 行为科学 行为科学
背景情况:
- 戒烟是全球公共卫生重点之一.
- 预测巴西吸烟者的治疗干预成功缺乏机器学习 (ML) 分析.
- 这项研究通过评估ML算法来预测戒烟成功的差距来弥补这一差距.
研究的目的:
- 评估八个ML算法的有效性,以预测戒烟治疗干预 (STI) 的成功.
- 确定影响巴西吸烟者戒烟结果的关键变量.
- 为加强戒烟计划提供数据驱动的见解.
主要方法:
- 利用了来自巴西吸烟者的12个变量数据集 (2006-2017年).
- 使用准确度,灵敏度,特异性,正预测值 (PPV) 和ROC曲线分析评估了八个ML算法.
- 分析了变量重要性,赔率比率 (OR) 和决策树流程图.
主要成果:
- 最好的模型,支持向量机,实现了0.726±0.031.03的PPV.
- 戒烟药物使用是最重要的预测因素 (OR4.42,重要性100.00).
- 复发的增加与成功正相关,而更高的香烟消费与失败相关.
结论:
- 机器学习模型可以有效地预测戒烟的成功.
- 戒烟药物使用和管理复发是成功戒烟的关键.
- 增加服务和药物治疗的机会可以降低吸烟率.
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