Decision Tree Model of Smoking Behaviour

Abo-Tabik, Maryam; Costen, Nicholas; Darby, John; Benn, Yael

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00311

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Several studies have identified CTs as a particularly effective method in smoking research [ 63 , 68 , 69 , 70 , 71 ]. CTs are a well-known ML algorithm that uses a tree structure to classify the data.…”

Section: Machine Learning Methods For Auto Intervention: the Future Of Smoking Cessation Appsmentioning

confidence: 99%

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Abo-Tabik

Benn

Costen

2021

Sensors

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Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.

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Section: Machine Learning Methods For Auto Intervention: the Future Of Smoking Cessation Appsmentioning

confidence: 99%

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Abo-Tabik

Benn

Costen

2021

Sensors

Self Cite

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“…Over the past two decades, several studies have been presented regarding this issue. Some of these are given in the following: Classification Trees (CTs) are useful machine-learning algorithms that can be used to classify data on tobacco use, according to the majority of research (Abo-Tabik, Costen, Darby and Benn, 2019;Coughlin, Tegge ,Sheffer, Bickel, 2020;Koslovsky, Swartz,Chan,Leon-Novelo,Wilkinson,Kendzor and Businelle, 2018;Zhang, Liu, Zhang, Huang, 2019). Dumortier, Beckjord, Shiffman and Sejdić, 2016 evaluated the desire to smoke based on 41 characteristics using data gathered from university students.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid machine learning approach in predicting smoking behaviour: The case of Turkey

Tezcan,

Karahan Adalı,

Özyurt Serim

2023

bmij

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This study aims to analyze the smoking behaviour of people aged 15 and older in Turkey using supervised and unsupervised machine learning methods. In this study, C4.5 and Random Forest (RF) were trained to predict smoking behaviour, and an apriori algorithm was used to detect associations. Sensitivity, specificity, accuracy, positive predicted value (PPV), and f-measure were used to compare the performances of the supervised models. The Turkey Health Interview Survey 2019 was used with a sample size of 17084 to predict smoking behaviour and determine the factors affecting smoking. Data analysis and performance evaluation were performed with R programming language by RStudio. By association rules, gender, age, and alcohol consumption are the most representative attributes of smoking behaviour. Associations were determined on smoking, non-smoking and quit-smoking behaviour. Also, it has been seen that the RF algorithm has better results than the C4.5 algorithm. It’s preferred to use the RF model, which had better performance with an accuracy of 0.909, a specificity of 0.965, a sensitivity of 0.782, a PPV of 0.908, and an f-measure of 0.840 for predicting smoking behaviour. This study contributes to the literature covering the most comprehensive national health survey data and using machine learning methods on this data in Turkey. Also, it indicates that machine learning methods can be used to analyze such datasets.

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Comparative Analysis of Machine Learning Algorithms for Predicting Mobile Price

Vamsi,

Mehrotra

2023

Lecture Notes in Networks and Systems

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Decision Tree Model of Smoking Behaviour

Cited by 5 publications

References 23 publications

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Are Machine Learning Methods the Future for Smoking Cessation Apps?

A hybrid machine learning approach in predicting smoking behaviour: The case of Turkey

Comparative Analysis of Machine Learning Algorithms for Predicting Mobile Price

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