Classification of nicotine-dependent users in India: a decision-tree approach

Singh, Akansha; Katyan, Himanshu

doi:10.1007/s10389-018-0973-x

Cited by 6 publications

(13 citation statements)

References 15 publications

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“…Nollen et al 11 also explored the relations between demographic, psychosocial factors, and tobacco to determine cigarette smokers at higher risk for alternative tobacco product use from a diverse sample of adult smokers. In 2019, Singh and Katyan 12 analyzed the GATS 2010 data to characterize nicotine dependency using decision tree approach.…”

Section: Introductionmentioning

confidence: 99%

Socio-demographic determinants of smoking: A data mining analysis of the Global Adult Tobacco Surveys

Durmuşoğlu¹,

Çiftçi²

2021

Türkiye Halk Sağlığı Dergisi

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Objective: This paper presented a) how the Global Adult Tobacco Surveys (GATSs) data can be used for extracting valuable information about tobacco use behaviors of people and b) the prediction performance of the implemented classification algorithms on the GATS data. Methods: Three well-known classification methods: K-nearest neighbor, C4.5 algorithm, and multilayer perceptron were applied to assess the classifying performance for the smoking status of GATS participants (pre-defined classes: smoker and no smoker) based on the socio-demographic characteristics (age group, gender, residence, education level, and working status). The first analysis was performed on the GATS data from Turkey. Subsequently, the model producing the best performance for Turkey was also implemented for other six European countries:

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Section: Introductionmentioning

confidence: 99%

Socio-demographic determinants of smoking: A data mining analysis of the Global Adult Tobacco Surveys

Durmuşoğlu¹,

Çiftçi²

2021

Türkiye Halk Sağlığı Dergisi

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“…The majority of these studies predicted a binary outcome related to smoking cessation, including the intention to quit smoking,90 adherence to nicotine replacement therapy,84 having high or low urges to smoke during a quit attempt88 and self-reported86 91 or lab-established85 cessation status. Other binary outcomes pertained to tobacco use patterns and history, including ever or current use of tobacco,81–83 87 nicotine dependence92 and whether individuals were exclusive or dual e-cigarette users 93. Only two continuous outcomes—time to first smoking lapse among recent cigarette quitters89 and biological age87—were examined.…”

Section: Resultsmentioning

confidence: 99%

“…Only two continuous outcomes—time to first smoking lapse among recent cigarette quitters89 and biological age87—were examined. In terms of ML algorithms, decision trees (n=6)85 88 90–92 94 and tree ensembles—including random forest82 86 87 93 and boosting tree86—were the most popular. Unsupervised clustering analysis was combined with a decision tree in one instance 90.…”

Section: Resultsmentioning

confidence: 99%

Machine learning applications in tobacco research: a scoping review

Kundu

Mitsakakis

et al. 2021

Tob Control

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ObjectiveIdentify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis.Data sourcesMEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded.Study selectionTwo reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis.Data extractionUsing data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted.Data synthesis74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed.ConclusionsML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.

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“…Compared with conventional regression, machine learning leverages computational power to reduce multicollinearity and improve the overall model performance. Applications of machine learning in tobacco research are emerging in recent years [20][21][22][23][24][25], but so far, only one such application has been on vaping behaviours. In this Holland-based study, a random forest model was used in conjunction with cross-sectional survey data to classify adult exclusive vapers from dual users of both cigarettes and e-cigarettes [25].…”

Section: Introductionmentioning

confidence: 99%

“…Here we present a simpler and more intuitive machine learning model-a classification tree-to identify and understand the importance of person-level correlates of current vaping. In other fields of tobacco research, classification trees have demonstrated good performance in predicting the status of lab-verified smoking cessation status [20], adherence to nicotine replacement therapy [23] and use of tobacco within 30-min of waking up [21]. Hence, we aimed to verify the performance of classification tree in vaping research and to provide actionable implications on policy interventions regarding e-cigarettes in a Canadian context.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to identify correlates of current e-cigarette use in Canada

Mitsakakis

Chaiton

2021

Exploration of Medicine

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Aim: Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm. Methods: This population-based cross-sectional study used the Canadian Tobacco, Alcohol, and Drugs Survey (CTADS) from 2017 that targeted residents aged 15 or older. Forty-six person-level characteristics were first screened in a logistic mixed-effects regression procedure for their strength in predicting vaper type (current vs. former vaper) among people who reported to have ever vaped. A 9:1 ratio was used to randomly split the data into a training set and a validation set. A classification tree model was developed using the cross-validation method on the training set using the selected predictors and assessed on the validation set using sensitivity, specificity and accuracy. Results: Of the 3,059 people with an experience of vaping, the average age was 24.4 years (standard deviation = 11.0), with 41.9% of them being female and 8.5% of them being aboriginal. There were 556 (18.2%) current vapers. The classification tree model performed relatively well and suggested attraction to e-cigarette flavors was the most important correlate of current vaping, followed by young age (< 18) and believing vaping to be less harmful to oneself than cigarette smoking. Conclusions: People who vape due to flavors are associated with very high risk of becoming current vapers. The findings of this study provide evidence that supports the ongoing ban on flavored vaping products in the US and suggests a similar regulatory intervention may be effective in Canada.

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Classification of nicotine-dependent users in India: a decision-tree approach

Cited by 6 publications

References 15 publications

Socio-demographic determinants of smoking: A data mining analysis of the Global Adult Tobacco Surveys

Socio-demographic determinants of smoking: A data mining analysis of the Global Adult Tobacco Surveys

Machine learning applications in tobacco research: a scoping review

A machine learning approach to identify correlates of current e-cigarette use in Canada

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