Classifying smoking urges via machine learning.

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.

Re

SUBMITTER: Dumortier A 

PROVIDER: S-EPMC5289882 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature