Publication: Técnicas de clasificación de data mining: una aplicación al consumo de tabaco en adolescentes
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Date
2014-05
Authors
Montaño-Moreno, Juan J. ; Gervilla-García, Elena ; Cajal-Blasco, Berta ; Palmer, Alfonso
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Publisher
Murcia: Universidad de Murcia, Editum
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info:eu-repo/semantics/article
Description
Abstract
El presente trabajo tiene el propósito de analizar el poder predictivo de diversas variables psicosociales y de personalidad sobre el consumo o no consumo de nicotina en la población adolescente mediante el uso de diversas técnicas de clasificación procedentes de la metodología Data Mining. Más concretamente, se analizan las RNA –Perceptrón Multicapa (MLP), Funciones de Base Radial (RBF) y Redes Probabilísticas (PNN)–, los árboles de decisión, el modelo de regresión logística y el análisis discriminante. Para ello, se ha trabajado con una muestra de 2666 adolescentes, de los cuales 1378 no consumen nicotina mientras que 1288 son consumidores de nicotina. Los modelos analizados han sido capaces de discriminar correctamente entre ambos tipos de sujeto en un rango comprendido entre el 77.39% y el 78.20%, alcanzando una sensibilidad del 91.29% y una especificidad del 74.32%. Con este estudio, se pone a disposición del especialista en conductas adictivas, un conjunto de técnicas estadísticas avanzadas capaces de manejar simultáneamente una gran cantidad de variables y sujetos, así como aprender de forma automática patrones y relaciones complejas, siendo muy adecuadas para la predicción y prevención del comportamiento adictivo.
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.
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.
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