Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis

Introduction: The main objective of this paper is to understand the decision to use electronic cigarette and vape (ECV) and vape among Malaysian adults by assessing the perceptions and demographic variables in relations to the current status (i.e., current, former, and never use). The predictive mod...

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Main Authors:	Kartiwi, Mira, Ab Rahman, Jamalludin, Nik Mohamed, Mohamad Haniki, Draman, Samsul, Ab Rahman, Norny Syafinaz
Format:	Article
Language:	English
Published:	Malaysian Medical Association 2017
Subjects:	R Medicine (General)
Online Access:	http://irep.iium.edu.my/58874/ http://irep.iium.edu.my/58874/ http://irep.iium.edu.my/58874/1/58874_Prediction%20of%20electronic%20cigarette.pdf

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spelling	iium-588742017-12-14T07:40:53Z http://irep.iium.edu.my/58874/ Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis Kartiwi, Mira Ab Rahman, Jamalludin Nik Mohamed, Mohamad Haniki Draman, Samsul Ab Rahman, Norny Syafinaz R Medicine (General) Introduction: The main objective of this paper is to understand the decision to use electronic cigarette and vape (ECV) and vape among Malaysian adults by assessing the perceptions and demographic variables in relations to the current status (i.e., current, former, and never use). The predictive model was developed using Induction Decision Tree (ID3) algorithm, a popular data mining technique an exploratory tool for knowledge discovery. Methods: The dataset was extracted from the National Electronic Cigarette Survey (NECS) 2016.A total of 4,288 responses were collected. The collected data was used to build and verified the model. Eight demographics variables (i.e., age, gender, race, religion, residence (urban/rural), marital, occupation and education) and twenty variables on perception of ECV were included as predictor variables. Results: By using the ID3 algorithm, it is possible to consider the relationship among variables and to identify the most informative variables for predicting the classification of the instance. It was identified that the most important variable is gender. This highlight that the decision for ECV use is significantly differ among male and female. The accuracy - i.e., percentage rate of right outcome - of the most optimum model generated in this study is 87.88%. Discussion: A number of interesting findings emerged from the ID3 model. Among others, the model indicated that young female (age < 32 years old) who perceived that ECV should be regulated than banned, and believe that ECV reduced coughing is more likely to be the current ECV user. Whereas among male, if the person is older than 44 years old, self-employed, lives in urban area, and agreed that ECV could reduce coughing, less addictive and reduced urge to smoke; he is predicted to be the current smoker. Hence, this study provides meaningful insights into understanding the different perceptions and characteristics between male and female current ECV users. Malaysian Medical Association 2017-08-10 Article NonPeerReviewed application/pdf en http://irep.iium.edu.my/58874/1/58874_Prediction%20of%20electronic%20cigarette.pdf Kartiwi, Mira and Ab Rahman, Jamalludin and Nik Mohamed, Mohamad Haniki and Draman, Samsul and Ab Rahman, Norny Syafinaz (2017) Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis. The Medical Journal of Malaysia, 72 (supp. 1). p. 34. ISSN 0300-5283 http://www.e-mjm.org/2017/v72s1/34.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	R Medicine (General)
spellingShingle	R Medicine (General) Kartiwi, Mira Ab Rahman, Jamalludin Nik Mohamed, Mohamad Haniki Draman, Samsul Ab Rahman, Norny Syafinaz Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
description	Introduction: The main objective of this paper is to understand the decision to use electronic cigarette and vape (ECV) and vape among Malaysian adults by assessing the perceptions and demographic variables in relations to the current status (i.e., current, former, and never use). The predictive model was developed using Induction Decision Tree (ID3) algorithm, a popular data mining technique an exploratory tool for knowledge discovery. Methods: The dataset was extracted from the National Electronic Cigarette Survey (NECS) 2016.A total of 4,288 responses were collected. The collected data was used to build and verified the model. Eight demographics variables (i.e., age, gender, race, religion, residence (urban/rural), marital, occupation and education) and twenty variables on perception of ECV were included as predictor variables. Results: By using the ID3 algorithm, it is possible to consider the relationship among variables and to identify the most informative variables for predicting the classification of the instance. It was identified that the most important variable is gender. This highlight that the decision for ECV use is significantly differ among male and female. The accuracy - i.e., percentage rate of right outcome - of the most optimum model generated in this study is 87.88%. Discussion: A number of interesting findings emerged from the ID3 model. Among others, the model indicated that young female (age < 32 years old) who perceived that ECV should be regulated than banned, and believe that ECV reduced coughing is more likely to be the current ECV user. Whereas among male, if the person is older than 44 years old, self-employed, lives in urban area, and agreed that ECV could reduce coughing, less addictive and reduced urge to smoke; he is predicted to be the current smoker. Hence, this study provides meaningful insights into understanding the different perceptions and characteristics between male and female current ECV users.
format	Article
author	Kartiwi, Mira Ab Rahman, Jamalludin Nik Mohamed, Mohamad Haniki Draman, Samsul Ab Rahman, Norny Syafinaz
author_facet	Kartiwi, Mira Ab Rahman, Jamalludin Nik Mohamed, Mohamad Haniki Draman, Samsul Ab Rahman, Norny Syafinaz
author_sort	Kartiwi, Mira
title	Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
title_short	Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
title_full	Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
title_fullStr	Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
title_full_unstemmed	Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis
title_sort	prediction of electronic cigarette and vape use among malaysian: decision tree analysis
publisher	Malaysian Medical Association
publishDate	2017
url	http://irep.iium.edu.my/58874/ http://irep.iium.edu.my/58874/ http://irep.iium.edu.my/58874/1/58874_Prediction%20of%20electronic%20cigarette.pdf
first_indexed	2023-09-18T21:23:19Z
last_indexed	2023-09-18T21:23:19Z
_version_	1777412026565394432

Prediction of electronic cigarette and vape use among Malaysian: decision tree analysis

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