Classifying smoking urges via machine learning
Classifying smoking urges via machine learning
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DOI：
10.1016/j.cmpb.2016.09.016
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发表时间：
2016-12-01
影响因子：
6.1
通讯作者：
Sejdic, Ervin
中科院分区：
文献类型：
--
作者：
Dumortier, Antoine;Beckjord, Ellen;Sejdic, Ervin
背景和目的：吸烟是发达国家最大的可预防的死亡和疾病原因，现代电子和机器学习的进步可以帮助我们以新的方式为吸烟者提供实时干预。在本文中，我们研究了不同的机器学习方法，使用与戒烟尝试期间有或没有吸烟冲动相关的情景特征，以准确地分类高冲动状态。方法：为了测试我们的机器学习方法，特别是贝叶斯，判别分析和决策树学习方法，我们使用了从300多名参与者中收集的数据集，他们发起了戒烟尝试。这三种分类方法进行了评估，观察灵敏度，特异性，准确性和precision.Results：结果的分析表明，基于特征选择的算法可以获得高的分类率，只有少数功能从整个数据集。分类树方法优于朴素贝叶斯和判别分析方法，分类的准确率高达86%。这些数字表明，机器学习可能是一个合适的方法来处理戒烟事宜，并预测吸烟的冲动，概述了一个潜在的使用移动的health applications.Conclusions：总之，机器学习分类器可以帮助识别吸烟的情况下，搜索最佳的功能和分类器参数显着提高算法的性能。此外，这项研究还支持新技术在改善戒烟干预效果、治疗师对时间和患者的管理以及优化可用医疗资源方面的有用性。未来的研究应侧重于通过开发能够提供实时干预的新型专家系统，在最短的时间内为真正需要的人提供更具适应性和个性化的支持。(C)2016爱思唯尔爱尔兰有限公司版权所有。
Background and objective: Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states.Methods: To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision.Results: The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications.Conclusions: In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. (C) 2016 Elsevier Ireland Ltd. All rights reserved.