Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques
Predicting mortality in COVID-19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Computational and Mathematical Methods |
| Online Access: | http://dx.doi.org/10.1155/2024/5891177 |
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| author | Tanzina Akter Md. Farhad Hossain Mohammad Safi Ullah Rabeya Akter |
| author_facet | Tanzina Akter Md. Farhad Hossain Mohammad Safi Ullah Rabeya Akter |
| author_sort | Tanzina Akter |
| collection | DOAJ |
| description | Predicting mortality in COVID-19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID-19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root-mean-square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author’s analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario. |
| format | Article |
| id | doaj-art-4f08e703ba73432ba4b0c6d264a09b60 |
| institution | Kabale University |
| issn | 2577-7408 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Computational and Mathematical Methods |
| spelling | doaj-art-4f08e703ba73432ba4b0c6d264a09b602025-01-03T01:44:18ZengWileyComputational and Mathematical Methods2577-74082024-01-01202410.1155/2024/5891177Mortality Prediction in COVID-19 Using Time Series and Machine Learning TechniquesTanzina Akter0Md. Farhad Hossain1Mohammad Safi Ullah2Rabeya Akter3Department of StatisticsDepartment of StatisticsDepartment of MathematicsDepartment of MathematicsPredicting mortality in COVID-19 is one of the most significant and difficult tasks at hand. This study compares time series and machine learning methods, including support vector machines (SVMs) and neural networks (NNs), to forecast the mortality rate in seven countries: the United States, India, Brazil, Russia, France, China, and Bangladesh. Data were gathered between December 31, 2019, when COVID-19 began, and March 31, 2021. The study used 457 observations with 4 variables: daily confirmed cases, daily deaths, daily mortality rate, and date. To predict the death rate in the seven countries that were chosen, the data were analyzed using time series analysis and machine learning techniques. Models were compared to obtain more accurate mortality predictions. The autoregressive integrated moving average (ARIMA) model with the lowest AIC value for each nation is found through time series analysis. By increasing the hidden layer and applying machine learning techniques, the NN model for each country is chosen, and the optimal model is determined by determining the model with the lowest error value. Additionally, SVM analyzes every country and calculates its R2 and root-mean-square error (RMSE). The lowest RMSE value is used to compare all of the time series and machine learning models. According to the comparison table, SVM provides a more accurate model to predict the mortality rate of the seven countries, with the lowest RMSE value. During the study period, mortality rates increased in Brazil and Russia and decreased in the United States, India, France, China, and Bangladesh, according to the comparison value of RMSE in this study. Furthermore, this paper shows that SVM outperforms all other models in terms of performance. According to the author’s analysis of the data, SVM is a machine learning technique that can be used to accurately predict mortality in a pandemic scenario.http://dx.doi.org/10.1155/2024/5891177 |
| spellingShingle | Tanzina Akter Md. Farhad Hossain Mohammad Safi Ullah Rabeya Akter Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques Computational and Mathematical Methods |
| title | Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques |
| title_full | Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques |
| title_fullStr | Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques |
| title_full_unstemmed | Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques |
| title_short | Mortality Prediction in COVID-19 Using Time Series and Machine Learning Techniques |
| title_sort | mortality prediction in covid 19 using time series and machine learning techniques |
| url | http://dx.doi.org/10.1155/2024/5891177 |
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