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Combined Neural Network Models for Epidemiological Data: Modelling Heterogeneity and Reduction of Input Correlations

  • M. H. Lamers
  • J. N. Kok
  • E. Lebret
Conference paper

Abstract

We consider an epidemiological dataset, concerned with predicting pulmonary response to air pollution. To gain more knowledge of nonlinear effects and interactions in the data, nonlinear neural network techniques were applied to model the data. Initially, we modelled the data with standard feedforward network models. Based on the epidemiologic effect of heterogeneity in response, we propose a novel combined neural network modelling strategy to improve prediction quality. Also, we propose the use of a neural network strategy for reducing correlation between covariants to improve modelling quality. The results presented are promising when compared to standard feedforward network modelling.

Keywords

Input Pattern Peak Expiratory Flow Feedforward Network Learn Vector Quantization Pulmonary Response 
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 Wien 1998

Authors and Affiliations

  • M. H. Lamers
    • 1
    • 2
  • J. N. Kok
    • 1
  • E. Lebret
    • 2
  1. 1.Computer Science DepartmentLeiden UniversityLeidenThe Netherlands
  2. 2.National Institute for Public Health and The Environment (RIVM)The Netherlands

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