Can Neural Networks Be Used to Define the Rules of Cardiovascular Disease Prevention in the Nutrition Domain?

  • Agnieszka Borowiec
  • Krzysztof Fronczyk
  • Bohdan Macukow
  • Maciej Grzenda
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


The primary objective of the work was to check the relation between household socio-economic characteristics and the corresponding food purchasing capabilities. The analysis has been based on the data collected in the national survey “Social Diagnosis 2000”. In order to search for possible dependencies between variables gathered in the survey, different classification methods have been applied.

Statistic methods of logistic regression analysis and discriminant analysis have been applied to model the discussed relation. However, only limited prediction efficiency has been observed. Therefore, neural networks-based methods have been applied. Evolutionary construction of multilayer perceptrons has been used to select both network architectures and weights. The method developed and tested previously on numerous prediction and classification problems has been used to provide classification models for the data discussed. Multilayer perceptrons have been shown to provide more precise classification models. Results of the network construction are presented together with final discussion.


Multilayer Perceptrons Data Pattern Food Category Load Forecast Social Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Agnieszka Borowiec
    • 1
  • Krzysztof Fronczyk
    • 1
  • Bohdan Macukow
    • 2
  • Maciej Grzenda
    • 2
  1. 1.Department of Health PromotionNational Institute of CardiologyWarszawaPoland
  2. 2.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarszawaPoland

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