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Related Experiment Video

Updated: Mar 8, 2026

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Classifying smoking urges via machine learning.

Antoine Dumortier1, Ellen Beckjord2, Saul Shiffman3

  • 1Department of Electrical and Computer Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA.

Computer Methods and Programs in Biomedicine
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Summary

Machine learning accurately predicts high-risk smoking urges using situational data, improving smoking cessation interventions. This technology can personalize support for quit attempts.

Keywords:
Feature selectionMachine learningSmoking cessationSmoking urgesSupervised learning

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Area of Science:

  • Computer Science
  • Public Health
  • Behavioral Science

Background:

  • Smoking remains a leading preventable cause of death and disease globally.
  • Modern technology, including machine learning (ML), offers novel ways for real-time intervention for smokers.
  • Identifying high-urge states is crucial for effective smoking cessation support.

Purpose of the Study:

  • To examine ML approaches for classifying high-urge smoking states using situational features.
  • To accurately predict moments of intense craving during a smoking quit attempt.

Main Methods:

  • Utilized a dataset from over 300 participants attempting to quit smoking.
  • Applied and evaluated three ML classification methods: Naive Bayes, discriminant analysis, and decision tree learning.
  • Assessed classification performance using sensitivity, specificity, accuracy, and precision.

Main Results:

  • Feature selection algorithms enabled high classification rates with minimal features.
  • Decision tree learning achieved the highest accuracy, up to 86%, outperforming Naive Bayes and discriminant analysis.
  • ML shows promise for predicting smoking urges and supporting smoking cessation.

Conclusions:

  • ML classifiers effectively identify high-risk smoking situations and improve intervention performance.
  • New technologies can enhance smoking cessation interventions and optimize healthcare resource management.
  • Future research should focus on adaptive, personalized, real-time interventions using expert systems.