Socio-demographic determinants of smoking: A data mining analysis of the Global Adult Tobacco Surveys

Abstract

Objective: This paper presented a) how the Global Adult Tobacco Surveys (GATSs) data can be used for extracting valuable information about tobacco use behaviors of people and b) the prediction performance of the implemented classification algorithms on the GATS data. Methods: Three well-known classification methods: K-nearest neighbor, C4.5 algorithm, and multilayer perceptron were applied to assess the classifying performance for the smoking status of GATS participants (pre-defined classes: smoker and no smoker) based on the socio-demographic characteristics (age group, gender, residence, education level, and working status). The first analysis was performed on the GATS data from Turkey. Subsequently, the model producing the best performance for Turkey was also implemented for other six European countries:

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

“…Similarly, it can be said that retired individuals tend to abstain from smoking due to the health challenges they may frequently encounter in their retirement years. These findings of this study are consistent with the findings of Durmuşoğlu and Kocabey (2021), Dumortier,Beckjord,Shiffman,Sejdić, (2016), and Singh and Katyan (2019. This study highlighted that gender and alcohol use are outstanding factors in determining smoker individuals.…”