Asthma has become the serious chronic and the most common disease of hospitalization in children. Recently, the number of children with asthma has increased year by year. Thereafter, the medical community pays much attention to the treatment of asthma. Because of noises or outlier, the resulting factors for asthma are complex. Traditional algorithms usually assume that asthma data are evenly distributed among various classes and might ignore minority classes. Therefore, an intelligent algorithm based on bacterial foraging optimization (BFO) and robust fuzzy algorithm (RFA) is applied to analyze asthma data in this paper. In the proposed algorithm, RFA with the property of robust can reduce the influence of noises or outlier. It can establish the fuzzy model and effectively analyze asthma data. For foraging theory, natural selection trends to eliminate animals with poor foraging strategies and to favor the propagation of genes for animals which have successful foraging strategies. BFO can model the mechanism of natural selection and find the best solution. Consequently, it can enhance the classification accuracy of asthma data. In this paper, asthma data were collected from Mackay Memorial Hospital in Taiwan to test the performance of the proposed algorithm. The performance of the proposed algorithm is supported by simulation results. From simulation results, the classification accuracy of the proposed algorithm outperforms other existing approaches and can help physicians to determine asthma.
International Journal of Fuzzy Systems – Springer Journals
Published: Jan 24, 2017
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