A machine-learning approach for predicting success in smoking cessation intervention

Davagdorj, Khishigsuren; Lee, Jong Seol; Park, Kwang-Ho; Ryu, Keun Ho

doi:10.1109/icawst.2019.8923252

Cited by 9 publications

(10 citation statements)

References 10 publications

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“…Similar to the study of Blok et al, [28], the "smokers at home" feature was highly important for construct decision support systems in our models. SMOTE with GBT and RF models were provided with similar significant features, such as "attendance in smoking cessation education", "occupation" and "age", the findings of which were similar with studies [22,53]. Aside from accurate model, interpretability refers to obtaining valuable information for the health care experts to make decisions as well as public healthcare concerns.…”

Section: Discussionmentioning

confidence: 71%

“…Our proposed experimental design is composed three components: In the first component, we selected a subset of 17 features with 3692 subjects that were done based on the data preprocessing and feature selection approach that covers lasso and multicollinearity analysis. Selected features were similar to studies [22,31,53] that were utilized in the KNHANES dataset. Aside from accurate models, selecting the representative features is an essential part of the medical domain to understand the significant risk factors.…”

Section: Discussionmentioning

confidence: 99%

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A Comparative Analysis of Machine Learning Methods for Class Imbalance in a Smoking Cessation Intervention

et al. 2020

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Smoking is one of the major public health issues, which has a significant impact on premature death. In recent years, numerous decision support systems have been developed to deal with smoking cessation based on machine learning methods. However, the inevitable class imbalance is considered a major challenge in deploying such systems. In this paper, we study an empirical comparison of machine learning techniques to deal with the class imbalance problem in the prediction of smoking cessation intervention among the Korean population. For the class imbalance problem, the objective of this paper is to improve the prediction performance based on the utilization of synthetic oversampling techniques, which we called the synthetic minority over-sampling technique (SMOTE) and an adaptive synthetic (ADASYN). This has been achieved by the experimental design, which comprises three components. First, the selection of the best representative features is performed in two phases: the lasso method and multicollinearity analysis. Second, generate the newly balanced data utilizing SMOTE and ADASYN technique. Third, machine learning classifiers are applied to construct the prediction models among all subjects and each gender. In order to justify the effectiveness of the prediction models, the f-score, type I error, type II error, balanced accuracy and geometric mean indices are used. Comprehensive analysis demonstrates that Gradient Boosting Trees (GBT), Random Forest (RF) and multilayer perceptron neural network (MLP) classifiers achieved the best performances in all subjects and each gender when SMOTE and ADASYN were utilized. The SMOTE with GBT and RF models also provide feature importance scores that enhance the interpretability of the decision-support system. In addition, it is proven that the presented synthetic oversampling techniques with machine learning models outperformed baseline models in smoking cessation prediction.

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Section: Discussionmentioning

confidence: 71%

Section: Discussionmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Methods for Class Imbalance in a Smoking Cessation Intervention

et al. 2020

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“…Sample size of these studies varied vastly from 92 to 2 million 83. Half of these studies used cross-sectional survey designs; the remaining studies used clinical trial data,84 longitudinal surveys,85 86 linked administrative data83 87 or participant records on a device 88 89…”

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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“…Therefore, it is important to concern the early detection of hypertension and finds significant risk factors regarding prevent suffering hypertension. In recent times, artificial intelligence-driven decision support systems have been developed in the field of health care [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset

Davagdorj

Lee

Park

et al. 2021

Advances in Intelligent Information Hiding and Multimedia Signal Processing

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Hypertension is a serious medical condition that significantly increases the risk of chronic diseases. Early detection of individuals at risk for hypertension allows to prevent and delay the incidence of related diseases and strokes. In recent years, numerous researches have been focused on the decision support system for predicting hypertension. However, the class imbalance has commonly occurred problem in real-world applications. In this paper, we present the end-to-end costsensitive neural network (COST-NN) framework incorporated with a weighted random forest-based feature selection technique to predict hypertension among Korean adults. First, it identifies the best representative features using a weighted random forest-based feature selection technique. Then, we apply the COST-NN for predicting target among hypertensive and non-hypertensive individuals. In order to identify the accurate predictive model, we compare the various baseline models. Experimental results showed that COST-NN outperforms the regular state-of-theart baseline models. In addition, the presented framework is expected to apply not only hypertension but also can support to prevent the patients from the risk of various diseases.

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A machine-learning approach for predicting success in smoking cessation intervention

Cited by 9 publications

References 10 publications

A Comparative Analysis of Machine Learning Methods for Class Imbalance in a Smoking Cessation Intervention

A Comparative Analysis of Machine Learning Methods for Class Imbalance in a Smoking Cessation Intervention

Machine learning applications in tobacco research: a scoping review

Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset

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