The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland
The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/7/1156 |
| _version_ | 1797528559898591232 |
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| author | Mohamed Yusuf Hassan |
| author_facet | Mohamed Yusuf Hassan |
| author_sort | Mohamed Yusuf Hassan |
| collection | DOAJ |
| description | The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values. |
| first_indexed | 2024-03-10T10:01:05Z |
| format | Article |
| id | doaj.art-d7c8258ba1f84816b353f5c249f0263e |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T10:01:05Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-d7c8258ba1f84816b353f5c249f0263e2023-11-22T01:59:13ZengMDPI AGSymmetry2073-89942021-06-01137115610.3390/sym13071156The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in SomalilandMohamed Yusuf Hassan0Department of Statistics, College of Business, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab EmiratesThe most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.https://www.mdpi.com/2073-8994/13/7/1156artificial intelligencetraining dataPearson correlationDickey–Fuller testlong short-term memorymachine learning |
| spellingShingle | Mohamed Yusuf Hassan The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland Symmetry artificial intelligence training data Pearson correlation Dickey–Fuller test long short-term memory machine learning |
| title | The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland |
| title_full | The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland |
| title_fullStr | The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland |
| title_full_unstemmed | The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland |
| title_short | The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland |
| title_sort | deep learning lstm and mtd models best predict acute respiratory infection among under five year old children in somaliland |
| topic | artificial intelligence training data Pearson correlation Dickey–Fuller test long short-term memory machine learning |
| url | https://www.mdpi.com/2073-8994/13/7/1156 |
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