Use of artificial neural networks in the prediction of kidney transplant outcomes

Fariba Shadabi, Robert Cox, Dharmendra Sharma, Nikolai Petrovsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Traditionally researchers have used statistical methods to predict medical outcomes. However, statistical techniques do not provide sufficient information 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 training including the danger of pre-processing data in such a way that misleading high predictive value is obtained.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRobert J. Howlett, Lakhmi C. Jain, Mircea Gh. Negoita
PublisherSpringer-Verlag
Pages566-572
Number of pages7
ISBN (Print)9783540232056
Publication statusPublished - 1 Jan 2004
Externally publishedYes
Event8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004 - Wellington, New Zealand
Duration: 20 Sep 200425 Sep 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3215
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004
CountryNew Zealand
CityWellington
Period20/09/0425/09/04

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