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Anales de Psicología

versión On-line ISSN 1695-2294versión impresa ISSN 0212-9728

Resumen

MONTANO-MORENO, Juan J.; GERVILLA-GARCIA, Elena; CAJAL-BLASCO, Berta  y  PALMER, Alfonso. Data mining classification techniques: an application to tobacco consumption in teenagers. Anal. Psicol. [online]. 2014, vol.30, n.2, pp.633-641. ISSN 1695-2294.  http://dx.doi.org/10.6018/analesps.30.2.160881.

This study is aimed at analysing the predictive power of different psychosocial and personality variables on the consumption or non-consumption of nicotine in a teenage population using different classification techniques from the field of Data Mining. More specifically, we analyse ANNs - Multilayer Perceptron (MLP), Radial Basis Functions (RBF) and Probabilistic Neural Networks (PNNs) - decision trees, the logistic regression model and discriminant analysis. To this end, we worked with a sample of 2666 teenagers, 1378 of whom do not consume nicotine while 1288 are nicotine consumers. The models analysed were able to discriminate correctly between both types of subjects within a range of 77.39% to 78.20%, achieving 91.29% sensitivity and 74.32% specificity. With this study, we place at the disposal of specialists in addictive behaviours a set of advanced statistical techniques that are capable of simultaneously processing a large quantity of variables and subjects, as well as learning complex patterns and relationships automatically, in such a way that they are very appropriate for predicting and preventing addictive behaviour.

Palabras clave : Artificial neural networks; nicotine; data mining; tobacco; logistic regression model; discriminant analysis.

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