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Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan

Author

Listed:
  • Stephanie Yang

    (Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei 106, Taiwan)

  • Hsueh-Chih Chen

    (Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei 106, Taiwan
    Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei 106, Taiwan
    Chinese Language and Technology Center, National Taiwan Normal University, Taipei 106, Taiwan
    MOST AI Biomedical Research Center, Tainan City 701, Taiwan)

  • Chih-Hsien Wu

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan)

  • Meng-Ni Wu

    (Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung 80756, Taiwan)

  • Cheng-Hong Yang

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

Abstract

The World Health Organization has urged countries to prioritize dementia in their public health policies. Dementia poses a tremendous socioeconomic burden, and the accurate prediction of the annual increase in prevalence is essential for establishing strategies to cope with its effects. The present study established a model based on the architecture of the long short-term memory (LSTM) neural network for predicting the number of dementia cases in Taiwan, which considers the effects of age and sex on the prevalence of dementia. The LSTM network is a variant of recurrent neural networks (RNNs), which possesses a special gate structure and avoids the problems in RNNs of gradient explosion, gradient vanishing, and long-term memory failure. A number of patients diagnosed as having dementia from 1997 to 2017 was collected in annual units from a data set extracted from the Health Insurance Database of the Ministry of Health and Welfare in Taiwan. To further verify the validity of the proposed model, the LSTM network was compared with three types of models: statistical models (exponential smoothing (ETS), autoregressive integrated moving average model (ARIMA), trigonometric seasonality, Box–Cox transformation, autoregressive moving average errors, and trend seasonal components model (TBATS)), hybrid models (support vector regression (SVR), particle swarm optimization–based support vector regression (PSOSVR)), and deep learning model (artificial neural networks (ANN)). The mean absolute percentage error (MAPE), root-mean-square error (RMSE), mean absolute error (MAE), and R-squared (R 2 ) were used to evaluate the model performances. The results indicated that the LSTM network has higher prediction accuracy than the three types of models for forecasting the prevalence of dementia in Taiwan.

Suggested Citation

  • Stephanie Yang & Hsueh-Chih Chen & Chih-Hsien Wu & Meng-Ni Wu & Cheng-Hong Yang, 2021. "Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan," Mathematics, MDPI, vol. 9(5), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:5:p:488-:d:506993
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    References listed on IDEAS

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    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Robert G. Brown & Richard F. Meyer, 1961. "The Fundamental Theorem of Exponential Smoothing," Operations Research, INFORMS, vol. 9(5), pages 673-685, October.
    3. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Li-Yeh Chuang & Yu-Da Lin & Hsueh-Wei Chang & Cheng-Hong Yang, 2012. "An Improved PSO Algorithm for Generating Protective SNP Barcodes in Breast Cancer," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
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    Cited by:

    1. Ali Unlu & Abdulhamit Subasi, 2025. "Substance use prediction using artificial intelligence techniques," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-40, February.

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