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

Fariba Shadabi, Robert Cox, Dharmendra Sharma, Nikolai Petrovsky

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

8 Citations (Scopus)


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
Original languageEnglish
Title of host publicationInternational Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004
EditorsFariba Shdabi, Dharmendra Sharma, Nikolai Petrovsky
Place of PublicationGermany
Number of pages7
ISBN (Print)9783540232056
Publication statusPublished - 2004
EventKnowledge-Based Intelligent Information and Engineering Systems - Wellington, New Zealand
Duration: 22 Sept 200424 Sept 2004


ConferenceKnowledge-Based Intelligent Information and Engineering Systems
Country/TerritoryNew Zealand


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