ArticleInternational journal of environmental research and public health2021
Development of Machine Learning Models for Prediction of Smoking Cessation Outcome.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders.Addiction (Abingdon, England) · 2023Trial
- Identifying Subgroups Among Current Smokers Enrolled in the Smoking Cessation Clinic Program: A Latent Class Analysis Approach.Healthcare (Basel, Switzerland) · 2026Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- A prediction model for classifying maternal pregnancy smoking using California state birth certificate information.Paediatric and perinatal epidemiology · 2024Article
- Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach.BMC public health · 2023Article
- Machine learning framework for atherosclerotic cardiovascular disease risk assessment.Journal of diabetes and metabolic disorders · 2023Article
- Predictors of smoking cessation outcomes identified by machine learning: A systematic review.Addiction neuroscience · 2023Article
- Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.PloS one · 2023Article
- Self-efficacy in predicting smoking cessation: A prospective study in Italy.Tobacco prevention & cessation · 2023Article
- The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2023Article
- Predicting outcomes of smoking cessation interventions in novel scenarios using ontology-informed, interpretable machine learning.Wellcome open research · 2023Article
- Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors.Journal of personalized medicine · 2022Article
- Classical and Neural Network Machine Learning to Determine the Risk of Marijuana Use.International journal of environmental research and public health · 2021Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
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.
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