Classifying smoking urges via machine learning

Antoine Dumortier; Ellen Beckjord; Saul Shiffman; Ervin Sejdić

doi:10.1016/j.cmpb.2016.09.016

Classifying smoking urges via machine learning

Comput Methods Programs Biomed. 2016 Dec:137:203-213. doi: 10.1016/j.cmpb.2016.09.016. Epub 2016 Sep 23.

Authors

Antoine Dumortier¹, Ellen Beckjord², Saul Shiffman³, Ervin Sejdić⁴

Affiliations

¹ Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA.
² Department of Psychiatry, University of Pittsburgh, 5115 Centre Avenue, Suite 140, Pittsburgh, PA 15232, USA.
³ Department of Psychology, University of Pittsburgh, 510 BELPB, 130 N. Bellefield Avenue, Pittsburgh, PA 15260, USA.
⁴ Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA. Electronic address: esejdic@ieee.org.

Abstract

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.

Keywords: Feature selection; Machine learning; Smoking cessation; Smoking urges; Supervised learning.

MeSH terms

Algorithms
Bayes Theorem
Humans
Machine Learning*
Models, Theoretical
Smoking*

Abstract

MeSH terms

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