Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

Cheng-Chien Lai, Wei-Hsin Huang, Betty Chia-Chen Chang and Lee-Ching Hwang
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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

IJERPH, 2021, vol. 18, issue 5, 1-10

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.

Keywords: smoking cessation; predictive model; machine learning; artificial neural network; precision medicine (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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