Uso De Fármacos Promove Aumento Na Cessação Do Tabagismo

Massago, Miyoko; Dantas, Maria Lúcia; Carolino, Idalina Diair Regla; Conegero, Celso Ivan

doi:10.22533/at.ed.26819180212

Ciências Da Saúde 2019

DOI: 10.22533/at.ed.26819180212

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Uso De Fármacos Promove Aumento Na Cessação Do Tabagismo

Miyoko Massago¹,

Maria Lúcia Dantas²,

Idalina Diair Regla Carolino³

et al.

Abstract: O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores. 2019Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterá-la de nenhuma forma ou utilizá-la para fins comerciais.

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2024

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References 56 publications

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Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers

Massago,

Iora

et al. 2024

PLoS ONE

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Smoking cessation is an important public health policy worldwide. However, as far as we know, there is a lack of screening of variables related to the success of therapeutic intervention (STI) in Brazilian smokers by machine learning (ML) algorithms. To address this gap in the literature, we evaluated the ability of eight ML algorithms to correctly predict the STI in Brazilian smokers who were treated at a smoking cessation program in Brazil between 2006 and 2017. The dataset was composed of 12 variables and the efficacies of the algorithms were measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and area under the receiver operating characteristic curve. We plotted a decision tree flowchart and also measured the odds ratio (OR) between each independent variable and the outcome, and the importance of the variable for the best model based on PPV. The mean global values for the metrics described above were, respectively, 0.675±0.028, 0.803±0.078, 0.485±0.146, 0.705±0.035 and 0.680±0.033. Supporting vector machines performed the best algorithm with a PPV of 0.726±0.031. Smoking cessation drug use was the roof of decision tree with OR of 4.42 and importance of variable of 100.00. Increase in the number of relapses also promoted a positive outcome, while higher consumption of cigarettes resulted in the opposite. In summary, the best model predicted 72.6% of positive outcomes correctly. Smoking cessation drug use and higher number of relapses contributed to quit smoking, while higher consumption of cigarettes showed the opposite effect. There are important strategies to reduce the number of smokers and increase STI by increasing services and drug treatment for smokers.

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Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers

Massago,

Iora

et al. 2024

PLoS ONE

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Uso De Fármacos Promove Aumento Na Cessação Do Tabagismo

Cited by 1 publication

References 56 publications

Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers

Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers

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