Abstract
Mortality has a vital role in population dynamics and is critical in a wide variety of fields, including demography, economics, and social sciences. This study aims to model and compare the mortality rate using two different models; the Lee-Carter model and Deep Neural Network (DNN). The sample data used is the case of the United Kingdom population. Mortality rates were modeled with the Lee-Carter model and deviance goodness of fit were used to test the model's suitability of the data. Next, mortality rates are modeled with the Deep Neural Network (DNN) and both models
are compared based on the mean square error (MSE) values. The results showed that the DNN model fits the best. Overall, we conclude that DNN approach appears to be a potential model to model and forecast population mortality.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
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Creators: | Creators Email / ID Num. Azim, Mohamad Hasif UNSPECIFIED Hussain, Saiful Izzuan sih@ukm.edu.my |
Subjects: | H Social Sciences > HA Statistics H Social Sciences > HA Statistics > Statistical data |
Divisions: | Universiti Teknologi MARA, Kedah > Sg Petani Campus |
Journal or Publication Title: | International Conference on Computing, Mathematics and Statistics (iCMS 2021) |
Event Title: | e-Proceedings of the 5th International Conference on Computing, Mathematics and Statistics (iCMS 2021) |
Event Dates: | 4-5 August 2021 |
Page Range: | pp. 53-59 |
Keywords: | Mortality, deep neural network, population |
Date: | 2021 |
URI: | https://ir.uitm.edu.my/id/eprint/56136 |