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The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, thi...

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Autores principales: Tsan, Yu-Tse, Chen, Der-Yuan, Liu, Po-Yu, Kristiani, Endah, Nguyen, Kieu Lan Phuong, Yang, Chao-Tung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835266/
https://www.ncbi.nlm.nih.gov/pubmed/35162879
http://dx.doi.org/10.3390/ijerph19031858
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author Tsan, Yu-Tse
Chen, Der-Yuan
Liu, Po-Yu
Kristiani, Endah
Nguyen, Kieu Lan Phuong
Yang, Chao-Tung
author_facet Tsan, Yu-Tse
Chen, Der-Yuan
Liu, Po-Yu
Kristiani, Endah
Nguyen, Kieu Lan Phuong
Yang, Chao-Tung
author_sort Tsan, Yu-Tse
collection PubMed
description This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.
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spelling pubmed-88352662022-02-12 The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA Tsan, Yu-Tse Chen, Der-Yuan Liu, Po-Yu Kristiani, Endah Nguyen, Kieu Lan Phuong Yang, Chao-Tung Int J Environ Res Public Health Article This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance. MDPI 2022-02-07 /pmc/articles/PMC8835266/ /pubmed/35162879 http://dx.doi.org/10.3390/ijerph19031858 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tsan, Yu-Tse
Chen, Der-Yuan
Liu, Po-Yu
Kristiani, Endah
Nguyen, Kieu Lan Phuong
Yang, Chao-Tung
The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA
title The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA
title_full The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA
title_fullStr The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA
title_full_unstemmed The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA
title_short The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA
title_sort prediction of influenza-like illness and respiratory disease using lstm and arima
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835266/
https://www.ncbi.nlm.nih.gov/pubmed/35162879
http://dx.doi.org/10.3390/ijerph19031858
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