This study is aimed at analysing the predictive power of different psychosocial and personality variables on the consumption or nonconsumption 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. © 2014: Servicio de Publicaciones de la Universidad de Murcia. Murcia (España).
CITATION STYLE
Montaño-Moreno, J. J., Gervilla-García, E., Cajal-Blasco, B., & Palmer, A. (2014). Técnicas de clasificación de data mining: Una aplicación al consumo de tabaco en adolescentes. Anales de Psicologia, 30(2), 633–641. https://doi.org/10.6018/analesps.30.2.160881
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