Abstract.
In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min–Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min–Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the ten-fold cross validation.
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Acknowledgements
We thank Dr U G Thirumaaran, Professor of Anesthesiology, Institute of Child Health and Hospital for Children/Madras Medical College, Chennai, Tamilnadu, India for providing the expert advice forming fuzzy temporal rules pertaining to medical diagnosis of heart and diabetic diseases.
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GANAPATHY, S., SETHUKKARASI, R., YOGESH, P. et al. An intelligent temporal pattern classification system using fuzzy temporal rules and particle swarm optimization. Sadhana 39, 283–302 (2014). https://doi.org/10.1007/s12046-014-0236-7
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DOI: https://doi.org/10.1007/s12046-014-0236-7