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.
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References
D. DeMers and G.W. Cottrell. Nonlinear dimensionality reduction. NIPS 5, pp.580–587, 1993.
S. Geman, E. Bienenstock, and R. Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4:1–58, 1992.
J. Hertz, A. Krogh, and R.G. Palmer. Introduction to the Theory of Neural Computation, volume 1 of Santa Fe Institute Studies in the Sciences of Complexity. Addison Wesley, 1991.
T. Kohonen. The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480, 1990.
L. Moseholm, E. Taudorf, and A. Frøsig. Pulmonary function changes in asthmatics associated with low-level SO2 and NO2 air pollution, weather, and medicine intake. Allergy, 48:334–344, 1993.
B.D. Ripley. Statistical aspects of neural networks. In Séminaire Européen de Statistique, 1992.
D.A. Savitz. In defense of black box epidemiology. Epidemiology, 5:550–552, 1994.
P. Skrabanek. The emptiness of the black box. Epidemiology, 5:553–555, 1994.
T. Waschulzik et al. Evaluation of an epidemiological data set as an example of the application of neural networks to the analysis of large medical data sets. 4 th Conf AI in Medicine Europe, pp.466–476, 1993.
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© 1998 Springer-Verlag Wien
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Lamers, M.H., Kok, J.N., Lebret, E. (1998). Combined Neural Network Models for Epidemiological Data: Modelling Heterogeneity and Reduction of Input Correlations. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_32
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_32
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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