Sigara kullanımının sosyo-demografik belirleyicileri: Küresel Yetişkin Tütün Araştırmaları üzerine bir veri madenciliği analizi

Year 2021, , 251 - 262, 02.12.2021

Zeynep Durmuşoğlu , Pınar Kocabey Çiftçi

https://doi.org/10.20518/tjph.884692

Abstract

Amaç: Bu makale a) Küresel Yetişkin Tütün Araştırması (KYTA) verilerinin tütün kullanım davranışları hakkındaki değerli bilgileri ortaya çıkarmada nasıl kullanılabileceğini ve b)KYTA verileri üzerinde uygulanan sınıflandırma algoritmalarının performanslarını sunmaktadır.Yöntem: Üç iyi bilinen sınıflandırma yöntemi olan K -en yakın komşu algoritması, C4.5 algoritması ve çok katmanlı algılayıcısı KYTA katılımcılarının sosyo-demografik özellikleri (yaş grubu, cinsiyet, yerleşim yeri, eğitim düzeyi ve çalışma durumu) temel alınarak, sigara içme durumunu (önceden tanımlanmış sınıflar: sigara içen ve içmeyen) doğru sınıflandırma performansı değerlendirilmiştir. İlk analiz KYTA Türkiye verileri üzerinde gerçekleştirilmiştir. Daha sonra Türkiye için en iyi performansı üreten model altı farklı Avrupa ülkesi: Yunanistan, Kazakistan, Polonya, Romanya, Rusya ve Ukrayna verileri için de uygulanmıştır.Bulgular: Bütün ağaç algoritmaları sigara içmeyenleri tespit etmekte daha doğru sonuçlar vermektedir. C4.5 algoritmasının doğru sınıflandırma oranı, Türkiye için en yüksek olandır. Ülkeler için yapılan karşılaştırmalı analiz, C4.5 algoritmasının Ukrayna’daki katılımcıların sigara içme durumunu %80’in üzerinde doğru bir şekilde sınıflandırabildiğini ancak Yunanistan için bu oranını %70’in altında
kaldığını göstermektedir.Sonuç: Bu makale, demografik veriler gibi KYTA tarafından
sağlanan bilgilerin, bir bireyin gelecekte sigara içmesi olasılığının hesaplanmasına
yardımcı olabileceğini ortaya koymaktadır

Keywords

tütün kullanımı, sigara içmek, kamu sağlığı

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: Greece, Kazakhstan, Poland, Romania, Russia, and Ukraine.Results: All of the tree algorithms were more confident to classify no smokers. The correct classification rate of C4.5 algorithm was the highest among the algorithms for the GATS Turkey data. In addition, the C4.5 algorithm classified the males more detailed than the females. The comparative analysis indicated that the C4.5 algorithm correctly classified the smoking status of participants of Ukraine over 80% while it was lower than 70% for Greece. Thus, the effects of demographic factors on smoking status can change from one country to another.Conclusion: This paper indicated that the data supplied by
GATS such as demographic data may help to compute the likelihood of an individual
to be a smoker in the future.

Keywords

Smoking, tobacco use, public health

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