دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Applicability of machine learning algorithm to predict the therapeutic intervention success in Brazilian smokers.
المؤلفون: Massago M; PhD Student in the Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil., Massago M; Master in Computer Sciences, State University of Maringa, Maringa, Parana, Brazil., Iora PH; Professor in the Morphological Sciences Department, State University of Maringa, Maringa, Parana, Brazil., Tavares Gurgel SJ; Professor in the Morphological Sciences Department, State University of Maringa, Maringa, Parana, Brazil., Conegero CI; Professor in the Department of Medicine, State University of Maringa, Maringa, Parana, Brazil., Carolino IDR; Professor in the Morphological Sciences Department, State University of Maringa, Maringa, Parana, Brazil., Mushi MM; Global Emergency Medicine Innovation and Implementation Research Center, Duke University School of Medicine, Duke Global Health Institute, Durham, North Carolina, United States of America., Chaves Forato GA; Master Student in the Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil., de Souza JVP; Assistant Professor of Emergency Medicine and Global Health, Duke Global Health Institute, Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America., Hernandes Rocha TA; Assistant Professor of Emergency Medicine and Global Health, Duke Global Health Institute, Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America., Bonfim S; PhD Student in the Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil., Staton CA; Assistant Professor of Emergency Medicine and Global Health, Duke Global Health Institute, Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America., Nihei OK; Professor in the Center of Education, Literature and Health, Western Parana State University, Foz do Iguaçu, Parana, Brazil., Vissoci JRN; Assistant Professor of Emergency Medicine and Global Health, Duke Global Health Institute, Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, United States of America., de Andrade L; Professor in the Postgraduate Program in Health Sciences, State University of Maringa, Maringa, Parana, Brazil.
المصدر: PloS one [PLoS One] 2024 Mar 04; Vol. 19 (3), pp. e0295970. Date of Electronic Publication: 2024 Mar 04 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Smokers* , Algorithms*, Humans ; Brazil/epidemiology ; Machine Learning ; Recurrence
مستخلص: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Massago et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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تواريخ الأحداث: Date Created: 20240304 Date Completed: 20240306 Latest Revision: 20240307
رمز التحديث: 20240307
مُعرف محوري في PubMed: PMC10911606
DOI: 10.1371/journal.pone.0295970
PMID: 38437221
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0295970