Data Mining techniques for drug use research

Abstract

[eng] Drug use motives are relevant to understand substance use amongst students. Data mining techniques present some advantages that can help to improve our understanding of drug use issue. The aim of this paper is to explore, through data mining techniques, the reasons why students use drugs. A random cluster sampling of schools was conducted in the island of Mallorca. Participants were 9300 students (52.9% girls) aged between 14 and 18 years old (M = 15.59, SD = 1.17). They answered an anonymous questionnaire about the frequency and type of drug used, as well as the motives. Five classifiers techniques are compared; all of them have much better performance (% of correct classifications) than the simplest classifier (more repeated category: drug use/never drug use) in all the compared drugs (alcohol, tobacco, cannabis, cocaine). Nevertheless, alcohol and tobacco have the lower percentage of correct classifications concerning the drug use motives, whereas these use motives have better classification performance when predicts cannabis and cocaine use. When we analyse the specific motives that better predicts the category classification (drug use/never drug use), the following reasons are highlighted in all of them: 'pleasant activity' (most frequent among drug users), and 'friends consume' and 'addiction' (both of them most frequent among never drug users). These results relate to the social dimension of drug use and agree with the statement that environmental context influences adolescent's involvement in risk behaviours. Implications of these results are discussed.