Next Article in Journal
A Dynamic Model of Cournot Competition for an Oligopolistic Market
Previous Article in Journal
A Multi-Criteria Computer Package-Based Energy Management System for a Grid-Connected AC Nanogrid
Article

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

1
Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei 106, Taiwan
2
Institute for Research Excellence in Learning Sciences, National Taiwan Normal University, Taipei 106, Taiwan
3
Chinese Language and Technology Center, National Taiwan Normal University, Taipei 106, Taiwan
4
MOST AI Biomedical Research Center, Tainan City 701, Taiwan
5
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807, Taiwan
6
Kaohsiung Medical University Chung-Ho Memorial Hospital, Kaohsiung 80756, Taiwan
7
Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
8
Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
*
Authors to whom correspondence should be addressed.
Academic Editor: Alessandro Niccolai
Mathematics 2021, 9(5), 488; https://doi.org/10.3390/math9050488
Received: 24 January 2021 / Revised: 14 February 2021 / Accepted: 23 February 2021 / Published: 27 February 2021
(This article belongs to the Section Mathematics and Computer Science)
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 (R2) 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. View Full-Text
Keywords: long short-term memory; dementia; prevalence; deep learning long short-term memory; dementia; prevalence; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Yang, S.; Chen, H.-C.; Wu, C.-H.; Wu, M.-N.; Yang, C.-H. Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan. Mathematics 2021, 9, 488. https://doi.org/10.3390/math9050488

AMA Style

Yang S, Chen H-C, Wu C-H, Wu M-N, Yang C-H. Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan. Mathematics. 2021; 9(5):488. https://doi.org/10.3390/math9050488

Chicago/Turabian Style

Yang, Stephanie, Hsueh-Chih Chen, Chih-Hsien Wu, Meng-Ni Wu, and Cheng-Hong Yang. 2021. "Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan" Mathematics 9, no. 5: 488. https://doi.org/10.3390/math9050488

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop