A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

  • Authors:
  • Muhammad A. Razi;Kuriakose Athappilly

  • Affiliations:
  • Department of Business Information Systems, Haworth College of Business, Western Michigan University, Kalamazoo, MI 49008-3821, USA;Department of Business Information Systems, Haworth College of Business, Western Michigan University, Kalamazoo, MI 49008-3821, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2005

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Abstract

Numerous articles comparing performances of statistical and Neural Networks (NNs) models are available in the literature, however, very few involved Classification and Regression Tree (CART) models in their comparative studies. We perform a three-way comparison of prediction accuracy involving nonlinear regression, NNs and CART models using a continuous dependent variable and a set of dichotomous and categorical predictor variables. A large dataset on smokers is used to run these models. Different prediction accuracy measuring procedures are used to compare performances of these models. The outcomes of predictions are discussed and the outcomes of this research are compared with the results of similar studies.