Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia
The Disease Control Division, Ministry of Health states that mortality data are a crucial tool for examining a community's health. Several studies have been conducted on developing a predictive model to predict the mortality rate but most of them are applying the standard machine learning model...
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| Language: | en |
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Penerbit UMP
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40002/1/Comparison%20of%20recurrent%20neural%20network%20and%20long%20short%20term%20memory.pdf http://umpir.ump.edu.my/id/eprint/40002/ https://doi.org/10.15282/daam.v4i1.9586 |
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| author | Anupriya, S. Nor Azuana, Ramli Awalin, L. J. |
| author_facet | Anupriya, S. Nor Azuana, Ramli Awalin, L. J. |
| author_sort | Anupriya, S. |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The Disease Control Division, Ministry of Health states that mortality data are a crucial tool for examining a community's health. Several studies have been conducted on developing a predictive model to predict the mortality rate but most of them are applying the standard machine learning models. Hence, this study is conducted with its main objective to develop the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models in predicting the general mortality rate in Malaysia and compare the predicted mortality rates between data with and without post-pandemic statistics. The death rate was investigated using the 2022 Revision of World Population Prospects, the twenty-seventh edition of official United Nations population estimates and projections issued by the Population Division of the United Nations Secretariat's Department of Economic and Social Affairs. A Z-test was employed to investigate if the 2021 and 2022 mortality rates bring any significant difference to the predicted mortality rates. The findings confirm the superiority of the LSTM model in accurately predicting mortality rates compared to RNN with the inclusion of post-pandemic data did not significantly affect the model's predictions. Overall, this study contributes to the understanding of Malaysian mortality rates and provides a foundation for future investigations in this field. The accuracy in predicting the future mortality rates, particularly in pandemic scenarios, remains challenging and requires further research. |
| format | Article |
| id | my.ump.umpir.40002 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2023 |
| publisher | Penerbit UMP |
| record_format | eprints |
| spelling | my.ump.umpir.400022024-01-15T03:53:30Z http://umpir.ump.edu.my/id/eprint/40002/ Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia Anupriya, S. Nor Azuana, Ramli Awalin, L. J. QA Mathematics The Disease Control Division, Ministry of Health states that mortality data are a crucial tool for examining a community's health. Several studies have been conducted on developing a predictive model to predict the mortality rate but most of them are applying the standard machine learning models. Hence, this study is conducted with its main objective to develop the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models in predicting the general mortality rate in Malaysia and compare the predicted mortality rates between data with and without post-pandemic statistics. The death rate was investigated using the 2022 Revision of World Population Prospects, the twenty-seventh edition of official United Nations population estimates and projections issued by the Population Division of the United Nations Secretariat's Department of Economic and Social Affairs. A Z-test was employed to investigate if the 2021 and 2022 mortality rates bring any significant difference to the predicted mortality rates. The findings confirm the superiority of the LSTM model in accurately predicting mortality rates compared to RNN with the inclusion of post-pandemic data did not significantly affect the model's predictions. Overall, this study contributes to the understanding of Malaysian mortality rates and provides a foundation for future investigations in this field. The accuracy in predicting the future mortality rates, particularly in pandemic scenarios, remains challenging and requires further research. Penerbit UMP 2023 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40002/1/Comparison%20of%20recurrent%20neural%20network%20and%20long%20short%20term%20memory.pdf Anupriya, S. and Nor Azuana, Ramli and Awalin, L. J. (2023) Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia. Data Analytics and Applied Mathematics (DAAM), 4 (1). pp. 30-39. ISSN 2773-4854. (Published) https://doi.org/10.15282/daam.v4i1.9586 https://doi.org/10.15282/daam.v4i1.9586 |
| spellingShingle | QA Mathematics Anupriya, S. Nor Azuana, Ramli Awalin, L. J. Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia |
| title | Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia |
| title_full | Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia |
| title_fullStr | Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia |
| title_full_unstemmed | Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia |
| title_short | Comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in Malaysia |
| title_sort | comparison of recurrent neural network and long-short term memory technique in predicting mortality rate in malaysia |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/40002/1/Comparison%20of%20recurrent%20neural%20network%20and%20long%20short%20term%20memory.pdf http://umpir.ump.edu.my/id/eprint/40002/ https://doi.org/10.15282/daam.v4i1.9586 https://doi.org/10.15282/daam.v4i1.9586 |
| url_provider | http://umpir.ump.edu.my/ |