Abstract
In this paper we propose an evolutionary fuzzy neural networks system for extracting knowledge from a set of time series containing medical information. The series represent isokinetic curves obtained from a group of patients exercising the knee joint on an isokinetic dynamometer. The system has two parts: i) it analyses the time series input in order generate a simplified model of an isokinetic curve; ii) it applies a grammar-guided genetic program to obtain a knowledge base represented by a fuzzy neural network. Once the knowledge base has been generated, the system is able to perform knee injuries detection. The results suggest that evolved fuzzy neural networks perform better than non-evolutionary approaches and have a high accuracy rate during both the training and testing phases. Additionally, they are robust, as the system is able to self-adapt to changes in the problem without human intervention.
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Couchet, J., Font, J.M., Manrique, D. (2009). Using Evolved Fuzzy Neural Networks for Injury Detection from Isokinetic Curves. In: Allen, T., Ellis, R., Petridis, M. (eds) Applications and Innovations in Intelligent Systems XVI. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-215-3_17
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DOI: https://doi.org/10.1007/978-1-84882-215-3_17
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