ArticleJournal of clinical medicine2021
Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Identifying factors associated with vaping cessation in young adults: A machine learning and XAI approach.PLOS digital health · 2026Article
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Model-based algorithms to ascertain smoking in administrative health data: a registry-based validation study.BMC medical research methodology · 2026Article
- An Innovative Approach for Extraction of Smoking Addiction Levels Using Physiological Parameters Based on Machine Learning: Proof of Concept.Diagnostics (Basel, Switzerland) · 2025Article
- Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.International journal of environmental research and public health · 2024Article
- Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Modelling the nicotine pharmacokinetic profile for e-cigarettes using real time monitoring of consumers' physiological measurements and mouth level exposure.BioData mining · 2024Article
- An ecological examination of early adolescent e-cigarette use: A machine learning approach to understanding a health epidemic.PloS one · 2024Article
- Prospective predictors of electronic nicotine delivery system initiation in tobacco naive young adults: A machine learning approach.Preventive medicine reports · 2023Article
- A machine learning approach to predict e-cigarette use and dependence among Ontario youth.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2022Article
- Prediction of Problematic Smartphone Use: A Machine Learning Approach.International journal of environmental research and public health · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies-Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as 'witnessed e-cigarette use in their household' and 'perception of their tobacco use', could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers.
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Registered trials
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