Projection of infant mortality rate in Malaysia using R
Projecting future infant mortality rate (IMR) is an important subject in ensuring the stability of health in one nation or a specific region in general. Secondary data of IMR from December 1950 until December 2020 from United Nations- World Population Prospects were used to project the trend of IMR...
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/19566/ http://journalarticle.ukm.my/19566/1/48436-177015-2-PB.pdf |
| _version_ | 1848814873862995968 |
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| author | Nurhasniza Idham Abu Hasan, Azlan Abdul Aziz, Mogana Darshini Ganggayah, Nur Faezah Jamal, Nor Mariyah Abdul Ghafar, |
| author_facet | Nurhasniza Idham Abu Hasan, Azlan Abdul Aziz, Mogana Darshini Ganggayah, Nur Faezah Jamal, Nor Mariyah Abdul Ghafar, |
| author_sort | Nurhasniza Idham Abu Hasan, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Projecting future infant mortality rate (IMR) is an important subject in ensuring the stability of health in one nation or a specific region in general. Secondary data of IMR from December 1950 until December 2020 from United Nations- World Population Prospects were used to project the trend of IMR in Malaysia up to 2023. In this study, five different forecasting models were adopted including Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, Exponential State Space model and Neural Network model. The results were analyzed using R programing and RStudio. The out-sample forecasts of mortality rates were evaluated using six error measures namely, Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE). Consequently, the keen analysis was focused on the trend and projection of infant mortality rate in the future using the most accurate model. The results showed that the “win” model for this study is ARIMA (0,2,0) model. The model provided a consistent estimate of IMR in relation to a similar decreasing pattern as shown by the original data and hence a reliable projection of IMR. The three ahead forecast values showed that IMR is likely to keep on continuously decreasing in the future. This study could become a guideline for human resource management and health care allocation planning. A forecast of IMR can help the implementation of interventions to reduce the burden of infant mortality within the target range. |
| first_indexed | 2025-11-15T00:41:00Z |
| format | Article |
| id | oai:generic.eprints.org:19566 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:41:00Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:195662022-09-05T08:06:04Z http://journalarticle.ukm.my/19566/ Projection of infant mortality rate in Malaysia using R Nurhasniza Idham Abu Hasan, Azlan Abdul Aziz, Mogana Darshini Ganggayah, Nur Faezah Jamal, Nor Mariyah Abdul Ghafar, Projecting future infant mortality rate (IMR) is an important subject in ensuring the stability of health in one nation or a specific region in general. Secondary data of IMR from December 1950 until December 2020 from United Nations- World Population Prospects were used to project the trend of IMR in Malaysia up to 2023. In this study, five different forecasting models were adopted including Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, Exponential State Space model and Neural Network model. The results were analyzed using R programing and RStudio. The out-sample forecasts of mortality rates were evaluated using six error measures namely, Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE). Consequently, the keen analysis was focused on the trend and projection of infant mortality rate in the future using the most accurate model. The results showed that the “win” model for this study is ARIMA (0,2,0) model. The model provided a consistent estimate of IMR in relation to a similar decreasing pattern as shown by the original data and hence a reliable projection of IMR. The three ahead forecast values showed that IMR is likely to keep on continuously decreasing in the future. This study could become a guideline for human resource management and health care allocation planning. A forecast of IMR can help the implementation of interventions to reduce the burden of infant mortality within the target range. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19566/1/48436-177015-2-PB.pdf Nurhasniza Idham Abu Hasan, and Azlan Abdul Aziz, and Mogana Darshini Ganggayah, and Nur Faezah Jamal, and Nor Mariyah Abdul Ghafar, (2022) Projection of infant mortality rate in Malaysia using R. Jurnal Sains Kesihatan Malaysia, 20 (1). pp. 23-36. ISSN 1675-8161 https://ejournal.ukm.my/jskm/issue/view/1402 |
| spellingShingle | Nurhasniza Idham Abu Hasan, Azlan Abdul Aziz, Mogana Darshini Ganggayah, Nur Faezah Jamal, Nor Mariyah Abdul Ghafar, Projection of infant mortality rate in Malaysia using R |
| title | Projection of infant mortality rate in Malaysia using R |
| title_full | Projection of infant mortality rate in Malaysia using R |
| title_fullStr | Projection of infant mortality rate in Malaysia using R |
| title_full_unstemmed | Projection of infant mortality rate in Malaysia using R |
| title_short | Projection of infant mortality rate in Malaysia using R |
| title_sort | projection of infant mortality rate in malaysia using r |
| url | http://journalarticle.ukm.my/19566/ http://journalarticle.ukm.my/19566/ http://journalarticle.ukm.my/19566/1/48436-177015-2-PB.pdf |