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
Original language | English |
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Title of host publication | International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004 |
Editors | Fariba Shdabi, Dharmendra Sharma, Nikolai Petrovsky |
Place of Publication | Germany |
Publisher | Springer |
Pages | 566-572 |
Number of pages | 7 |
ISBN (Print) | 9783540232056 |
DOIs | |
Publication status | Published - 2004 |
Event | Knowledge-Based Intelligent Information and Engineering Systems - Wellington, New Zealand Duration: 22 Sept 2004 → 24 Sept 2004 |
Conference
Conference | Knowledge-Based Intelligent Information and Engineering Systems |
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Country/Territory | New Zealand |
City | Wellington |
Period | 22/09/04 → 24/09/04 |