Asthmatic attacks prediction considering weather factors based on Fuzzy-AR model

Asthma causes the bronchus inflammation, and makes breathing impossible. In worst case, asthma leads to death due to dyspnea. If we can predict that children cause asthmatic attacks, they can prevent from asthmatic attacks with minimum attention. Therefore, asthmatic attacks prediction system is desired. As a prediction system using time series data, there is Fuzzy-AR model that can consider multi factors. In this paper, we propose a prediction method of the number of asthmatic attacks on next month based on Fuzzy-AR model. The proposed method considers weather factors; temperature, atmospheric pressure and humidity data. This method is applied to asthmatic attacks data from Himeji city Medical Association. As a comparison method, AR model is applied to same data. The experimental results shown that the proposed method predicts the number of asthmatic attacks better than AR model.

[1]  Will Gersch,et al.  AR model prediction of time series with trends and seasonalities: A contrast with Box-Jenkins modeling , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[2]  H. Akaike A new look at the statistical model identification , 1974 .

[3]  Yutaka Hata,et al.  A fuzzy logic approach to predict human body weight based on AR model , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[4]  Shogo Nishida,et al.  Evolutionary fuzzy ARTMAP for autoregressive model order selection and classification of EEG signals , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[5]  R. Shibata Selection of the order of an autoregressive model by Akaike's information criterion , 1976 .

[6]  Jingyu Liu,et al.  Human cardiovascular system identification and application using a hybrid method of auto-regression and neuro-fuzzy inference systems , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[7]  Bor-Sen Chen,et al.  Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach , 2000, IEEE Trans. Fuzzy Syst..

[8]  N. Watanabe,et al.  A fuzzy rule based time series model , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[9]  J. Wang,et al.  Degradation prediction method by use of autoregressive algorithm , 2008, 2008 IEEE International Conference on Industrial Technology.

[10]  Jeffrey M. Hausdorff,et al.  Time series modeling of heart rate dynamics , 1993, Proceedings of Computers in Cardiology Conference.