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Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

Author

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  • Cheng-Chien Lai

    (Department of Medical Education, Taipei Veterans General Hospital, Taipei City 11217, Taiwan)

  • Wei-Hsin Huang

    (Department of Family Medicine, Mackay Memorial Hospital 25160, Taipei City 11217, Taiwan)

  • Betty Chia-Chen Chang

    (Department of Family Medicine, Mackay Memorial Hospital 25160, Taipei City 11217, Taiwan)

  • Lee-Ching Hwang

    (Department of Family Medicine, Mackay Memorial Hospital 25160, Taipei City 11217, Taiwan
    Department of Medicine, Mackay Medical College, New Taipei City 25245, Taiwan)

Abstract

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

Suggested Citation

  • Cheng-Chien Lai & Wei-Hsin Huang & Betty Chia-Chen Chang & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Smoking Cessation Outcome," IJERPH, MDPI, vol. 18(5), pages 1-10, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2584-:d:510848
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    References listed on IDEAS

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    1. IfeanyiChukwu O. Onor & Daniel L. Stirling & Shandrika R. Williams & Daniel Bediako & Amne Borghol & Martha B. Harris & Tiernisha B. Darensburg & Sharde D. Clay & Samuel C. Okpechi & Daniel F. Sarpong, 2017. "Clinical Effects of Cigarette Smoking: Epidemiologic Impact and Review of Pharmacotherapy Options," IJERPH, MDPI, vol. 14(10), pages 1-16, September.
    2. Shigeyuki Hamori & Minami Kawai & Takahiro Kume & Yuji Murakami & Chikara Watanabe, 2018. "Ensemble Learning or Deep Learning? Application to Default Risk Analysis," JRFM, MDPI, vol. 11(1), pages 1-14, March.
    3. Wei-Hsin Huang & Hsin-Yin Hsu & Betty Chia-Chen Chang & Fong-Ching Chang, 2018. "Factors Correlated with Success Rate of Outpatient Smoking Cessation Services in Taiwan," IJERPH, MDPI, vol. 15(6), pages 1-7, June.
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    Cited by:

    1. Laura Zoboroski & Torrey Wagner & Brent Langhals, 2021. "Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use," IJERPH, MDPI, vol. 18(14), pages 1-15, July.

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