Predicting smoking cessation success: a machine learning approach using the global adult tobacco survey
Abstract
Objective: This paper aimed to evaluate the effectiveness of machine learning (ML) algorithms
in predicting smoking cessation outcomes using Global adult tobacco surveys (GATS) data.
Specifically, we investigated the influence of sociodemographic, behavioral, and environmental
factors on smoking cessation success.
Method: GATS data from multiple countries were analyzed using various ML models, including:
K-nearest neighbors, decision trees, random forests, neural networks, Naive Bayes and logistic
regression. These models were utilized to classify smoking behaviors and predict smoking cessation
outcomes.
Results: Logistic regression exhibited the highest accuracy (69.8%) in predicting smoking cessation,
surpassing other ML models and emphasizing the impact of sociodemographic, behavioral, and
environmental factors on cessation. Additionally, the study highlights the role of education,
employment, and the family environment in shaping smoking behaviors and cessation outcomes.
Conclusion: This research underscores the potential of ML to inform effective smoking cessation
strategies. By identifying influential factors and predicting cessation outcomes, policymakers can
tailor interventions to specific populations and enhance their effectiveness.
Keywords
Smoking cessation, Machine learning, Predictive models, Data analysis, Global adult tobacco survey
Ethical Statement
Since secondary data were used
in the study, there is no need for ethics committee
approval.
References
- 1. Warren KB, Devin CT, William HC, al. Predictors of smoking cessation outcomes identified by machine learning:A systematic review. Addict Neurosci 2023;6:100068. 2. Lai CC, Huang WH, Chang BC, Hwang LC. Development of Machine Learning Models for Prediction of Smoking Cessation Outcome. Int J Environ Res Public Health 2021;18(5):2584.
- 3. Rijhwani K, Mohanty VR, Aswini YB, Singh V, Hashmi S. Applicability of Data Mining and Predictive Analysis for Tobacco Cessation: An Exploratory Study. Front Dent 2020;17:24.
- 4. Thangaraju P, Barkavi G, Karthikeyan T. Mining Lung Cancer Data for Smokers and Non-Smokers by Using Data Mining Techniques. IJARCCE. 2014;3(7):7622-7626.
- 5. Kharabsheh M, Qawasmeh A, Megdadi O, Jawabreh N, Mudallal R, Alzyoud S. A Critical Analysis of the Relationship between Depression and Smoking using Machine Learning. IJSTR 2019;8(12):22-26.
- 6. Roy P, Hossain MF, Jahan N. Machine Learning Approach to Predict Influence of Smoking on Student Life. ICCCNT Proc. 2021;1-6.
- 7. Ismail RM. Using machine learning algorithms to study the smoking behavior of Iraqi students. Eurasian Research Bulletin 2023;16:91-101.
- 8. Choi J, Jung H.T, Ferrell A, Woo S, Haddad L. Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users. J Clin Med 2021;10(5):972.
- 9. Kharabsheh M, Meqdadi O, Alabed M, Veeranki S, Abbadi A, Alzyoud S. A Machine Learning Approach for Predicting Nicotine Dependence. IJACSA 2019;10(3):179-184.
- 10. Davagdorj K, Yu SH, Kim SY, et al. Prediction of 6 Months Smoking Cessation Program among Women in Korea. Int J of Machine Learning and Computing 2019;9(1):83-90.
- 11. Thakur SS, Poddar P, Roy RB. Real-time prediction of smoking activity using machine learning based multi-class classification model. Multimed Tools Appl 2022;81(10):14529-14551.