Abstract
Hand, foot and mouth disease (HFMD) is a common infectious disease in global public health. In this paper, the time series decomposition and heterogeneous data fusion (TDDF) method is proposed to enhance features in the performance of HFMD outpatients prediction. The TDDF first represents meteorological features and Baidu search index features with the consideration of lags, then those features are fused into decomposed historical HFMD cases to predict coming outpatient cases. Experimental results and analyses on the real collected records show the efficiency and effectiveness of TDDF on regression methods.
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Acknowledgements
This work was supported by the Natural Science Foundation of Fujian Province of China (No. 2018J01539 and No. 2019J01713), and the Xiamen Center for Disease Control and Prevention. The authors would like to thank the editor and anonymous reviewers for their helpful comments in improving the quality of this paper.
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Wang, Z., Huang, Y., He, B., Luo, T., Wang, Y., Lin, Y. (2019). TDDF: HFMD Outpatients Prediction Based on Time Series Decomposition and Heterogenous Data Fusion in Xiamen, China. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_48
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