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Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigare...

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Autores principales: Issabakhsh, Mona, Sánchez-Romero, Luz Maria, Le, Thuy T. T., Liber, Alex C., Tan, Jiale, Li, Yameng, Meza, Rafael, Mendez, David, Levy, David T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249849/
https://www.ncbi.nlm.nih.gov/pubmed/37289765
http://dx.doi.org/10.1371/journal.pone.0286883
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author Issabakhsh, Mona
Sánchez-Romero, Luz Maria
Le, Thuy T. T.
Liber, Alex C.
Tan, Jiale
Li, Yameng
Meza, Rafael
Mendez, David
Levy, David T.
author_facet Issabakhsh, Mona
Sánchez-Romero, Luz Maria
Le, Thuy T. T.
Liber, Alex C.
Tan, Jiale
Li, Yameng
Meza, Rafael
Mendez, David
Levy, David T.
author_sort Issabakhsh, Mona
collection PubMed
description Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.
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spelling pubmed-102498492023-06-09 Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study Issabakhsh, Mona Sánchez-Romero, Luz Maria Le, Thuy T. T. Liber, Alex C. Tan, Jiale Li, Yameng Meza, Rafael Mendez, David Levy, David T. PLoS One Research Article Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US. Public Library of Science 2023-06-08 /pmc/articles/PMC10249849/ /pubmed/37289765 http://dx.doi.org/10.1371/journal.pone.0286883 Text en © 2023 Issabakhsh et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Issabakhsh, Mona
Sánchez-Romero, Luz Maria
Le, Thuy T. T.
Liber, Alex C.
Tan, Jiale
Li, Yameng
Meza, Rafael
Mendez, David
Levy, David T.
Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study
title Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study
title_full Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study
title_fullStr Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study
title_full_unstemmed Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study
title_short Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study
title_sort machine learning application for predicting smoking cessation among us adults: an analysis of waves 1-3 of the path study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249849/
https://www.ncbi.nlm.nih.gov/pubmed/37289765
http://dx.doi.org/10.1371/journal.pone.0286883
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