Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

Journal article

PURPOSE: New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the d...

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Authors: Catto, J; Linkens, D; Abbod, M; Chen, M; Burton, J

• Publication status: Published
• Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
• Volume: 9
• Issue: 11
• Pages: 4172-4177
• Publication date: 2003-09-01
• ISSN: 1078-0432
• Language: English
• Keywords: Treatment Outcome; Base Pair Mismatch; Fuzzy Logic; Urinary Bladder Neoplasms; Time Factors; Neural Networks (Computer); Smoking; Artificial Intelligence; Risk Factors; Recurrence; Humans; Predictive Value of Tests; Carcinoma, Transitional Cell