Rule Evolving System for Knee Lesion Prognosis from Medical Isokinetic Curves
This paper proposes a system for applying data mining to a set of time series with medical information. The series represent an isokinetic curve that is obtained from a group of patients performing a knee exercise on an isokinetic machine. This system has two steps: the first one is to analyze the input time series in order to generate a simplified model of an isokinetic curve; the second step applies a grammar-guided genetic program including an evolutionary gradient operator and an entropy-based fitness function to obtain a set of rules for a knowledge-based system. This system performs medical prognosis for knee injury detection. The results achieved have been statistically compared to another evolutionary approach that generates fuzzy rule-based systems.
KeywordsIsokinetic curves medical time series grammar-guided genetic programming data-mining evolutionary gradient
Unable to display preview. Download preview PDF.
- 3.Podgorelec, V., Kokol, P., Stiglic, M., Hericko, M., Rozman, I.: Knowledge discovery with classification rules in a cardiovascular dataset. Computer Methods and Programs in Biomedicine 80, S39–S49 (2005)Google Scholar
- 4.Ohsaki, M., Yokoi, H., Abe, M., Tsumoto, S., Yamaguchi, T.: Proposal of medical kdd support user interface utilizing rule interestingness measures. In: Proc. of Sixth IEEE International Conference on Data Mining, pp. 759–764 (2006)Google Scholar
- 6.Couchet, J., Font, J.M., Manrique, D.: Using evolved fuzzy neural networks for injury detection from isokinetic curves. In: Proc. of the Twenty-Eighth SGAI International Conference, AI 2008, pp. 225–238 (2008)Google Scholar
- 8.Whigham, P.: Grammatically-based genetic programming. In: Proc. of the Workshop on Genetic Programming: From Theory to Real-World Apps, pp. 33–41 (1995)Google Scholar
- 9.Couchet, J., Manrique, D., Ríos, J., Rodríguez-Patón, A.: Crossover and mutation operators for grammar-guided genetic programming. Soft Computing - A Fusion of Foundations, Methodologies and Applications 11(10), 943–955 (2007)Google Scholar