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Use of Artificial Neural Networks in the Prediction of Kidney Transplant Outcomes

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))

Abstract

Traditionally researchers have used statistical methods to predict medical outcomes. However, statistical techniques do not provide sufficient in-formation for solving problems of high complexity. Recently more attention has turned to a variety of artificial intelligence modeling techniques such as Artificial Neural Networks (ANNs), Case Based Reasoning (CBR) and Rule Induction (RI). In this study we sought to use ANN to predict renal transplantation outcomes. Our results showed that although this was possible, the positive predictive power of the trained ANN was low, indicating a need for improvement if this approach is to be useful clinically. We also highlight potential problems that may arise when using incomplete clinical datasets for ANN train-ing including the danger of pre-processing data in such a way that misleading high predictive value is obtained.

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Author information

Authors and Affiliations

  1. Division of Health, Design and Science, Medical Informatics Center, USA

    Fariba Shadabi & Nikolai Petrovsky

  2. School of Information Sciences and Engineering, University of Canberra, ACT, 2601, Australia

    Robert Cox & Dharmendra Sharma

Authors
  1. Fariba Shadabi
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  2. Robert Cox
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  3. Dharmendra Sharma
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  4. Nikolai Petrovsky
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Shadabi, F., Cox, R., Sharma, D., Petrovsky, N. (2004). Use of Artificial Neural Networks in the Prediction of Kidney Transplant Outcomes. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_76

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  • DOI: https://doi.org/10.1007/978-3-540-30134-9_76

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

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