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Application of optimized LSTM in prediction of the cumulative confirmed cases of COVID-19.
- Source :
-
Computer methods in biomechanics and biomedical engineering [Comput Methods Biomech Biomed Engin] 2024 Oct; Vol. 27 (13), pp. 1893-1905. Date of Electronic Publication: 2023 Oct 03. - Publication Year :
- 2024
-
Abstract
- This paper proposes an optimized Long Short-Term Memory (LSTM+) model for predicting cumulative confirmed cases of COVID-19 in Germany, the UK, Italy, and Japan. The LSTM+ model incorporates two key optimizations: (1) fine-adjustment of parameters and (2) a 're-prediction' process that utilizes the latest prediction results from the previous iteration. The performance of the LSTM+ model is evaluated and compared with that of Backpropagation (BP) and traditional LSTM models. The results demonstrate that the LSTM+ model significantly outperforms both BP and LSTM models, achieving a Mean Absolute Percentage Error (MAPE) of less than 0.6%. Additionally, two illustrative examples employing the LSTM+ model further validate its general applicability and practical performance for predicting cumulative confirmed COVID-19 cases.
- Subjects :
- Humans
SARS-CoV-2
Germany epidemiology
Japan epidemiology
COVID-19 epidemiology
Subjects
Details
- Language :
- English
- ISSN :
- 1476-8259
- Volume :
- 27
- Issue :
- 13
- Database :
- MEDLINE
- Journal :
- Computer methods in biomechanics and biomedical engineering
- Publication Type :
- Academic Journal
- Accession number :
- 37787059
- Full Text :
- https://doi.org/10.1080/10255842.2023.2264438