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The Role of the Artificial Neural Network in the Characterisation of Complex Systems and the Prediction of Disease

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Artificial Neural Networks in Biomedicine

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

The physical sciences have, for a number of years, identified certain systems as ‘complex’. Although the definition of complexity has not been precisely defined, its general nature has. Complex systems are those entities that elude simple reductionist analysis because of their unpredictable non-cause and effect behaviour. These systems, which appear chaotic, can often be characterised by non-Newtonian, non-Cartesian, non-linear analysis. The behaviour of the human organism, in many respects, fulfills the broad definition of a complex system. For years, biomedical science has sought to understand the human organism by way of classic linear analysis. Such characterisation has often proved to be wanting. The artificial neural network is a powerful non-linear paradigm for the recognition of complex patterns. With this in mind, it appears to be fitting that the application of the artificial neural network to some more complicated human biomedical problems might be more successful than the traditional linear approaches used in the past. Described here is the progress that has been made in the application of the artificial neural network to the analysis of human disease. Covered herein is a brief review of the manifold applications of the network to the prediction of the presence of specific disease processes as well as the prediction of outcome in such processes. Although most of these are reports of early studies on small numbers of patients, they virtually all show that the artificial neural network outperforms the established paradigms for the same analysis. Nonetheless, more work will be required before the actual worth of this new paradigm is established.

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  1. Department of Emergency Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA

    William G. Baxt M.D.

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Editors and Affiliations

  1. School of Computing and Mathematical Sciences, Liverpool John Moore’s University, Liverpool, L3 3AF, UK

    Paulo J. G. Lisboa BSc, PhD

  2. School of Electronic, Communication and Electrical Engineering, University of Plymouth, Plymouth, UK

    Emmanuel C. Ifeachor BSc, MSc, PhD

  3. Institute of Computer Science, Technical University of Lodz, Lodz, Poland

    Piotr S. Szczepaniak MSc, PhD, DSc

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Baxt, W.G. (2000). The Role of the Artificial Neural Network in the Characterisation of Complex Systems and the Prediction of Disease. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_3

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  • DOI: https://doi.org/10.1007/978-1-4471-0487-2_3

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