Decision tree models for characterizing smoking patterns of older adults

• Published in: Expert systems with applications Vol. 39; no. 1; pp. 445 - 451
• Main Authors: Moon, Sung Seek, Kang, Suk-Young, Jitpitaklert, Weerawat, Kim, Seoung Bum
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2012
• Subjects: Adults; Age; Cigarettes; Classification; Data mining; Decision trees; Expert systems; Health; Older adults; Smoking; Smoking patterns; Smoking patterns; Data mining; Decision trees; Older adults
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2011.07.035

► We conduct data mining to characterize smoking behavior among older adults. ► The age when first started smoking cigarettes is the most important factor. ► Social workers need to provide more customized intervention to older adults. 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.