Decision tree models for characterizing smoking patterns of older adults

  • Authors:

  • Sung Seek Moon;Suk-Young Kang;Weerawat Jitpitaklert;Seoung Bum Kim

  • Affiliations:

  • School of Social Work, University of Texas at Arlington, Arlington, TX, USA;Department of Social Work, Binghamton University, State University of New York, NY, USA;Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, TX, USA;School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

The main objective of the present paper is to characterize smoking behavior among older adults by assessing the psychological distress, physical health status, alcohol use, and demographic variables in relations to the current smoking. We targeted 466 senior American smokers who are 65 years of age or older from the 2006 National Survey on Drug Use and Health (NSDUH, 2006). We employed a decision tree algorithm to conduct classification analysis to find the relationship between the average numbers of cigarette use per day. The results showed that the most important explanatory variable for prediction of the average number of cigarette use per day is the age when first started smoking cigarettes every day, followed by education level, and psychological distress. These results suggest that social workers need to provide more customized and individualized intervention to older adults.