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Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob

This research integrates the Lee-Carter (LC) model with neural network (NN) methods to enhance mortality forecasting. The LC model, widely used for demographic forecasting, has limitations in capturing complex and nonlinear mortality trends. To address these limitations, we incorporate NN methods, n...

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Main Authors: Abd Hamid, Hana Natasya, Khairul Hizam, Khairunnisa, Yaacob, Nurul Aityqah
Format: Article
Language:English
Published: Universiti Teknologi MARA, Negeri Sembilan 2024
Subjects:
L Education (General)
QA Mathematics
Online Access:https://ir.uitm.edu.my/id/eprint/106004/1/106004.pdf
https://ir.uitm.edu.my/id/eprint/106004/
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spelling my.uitm.ir.1060042024-11-17T15:48:16Z https://ir.uitm.edu.my/id/eprint/106004/ Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob Abd Hamid, Hana Natasya Khairul Hizam, Khairunnisa Yaacob, Nurul Aityqah L Education (General) QA Mathematics This research integrates the Lee-Carter (LC) model with neural network (NN) methods to enhance mortality forecasting. The LC model, widely used for demographic forecasting, has limitations in capturing complex and nonlinear mortality trends. To address these limitations, we incorporate NN methods, namely a multilayer feed-forward neural network (MFFNN), to identify patterns within mortality data. The study evaluates the performance of the LC and LC-NN models across five countries: Germany, Japan, Malaysia, South Korea, and the United States of America (USA). Findings indicate that the LC-NN model outperforms the LC model, as demonstrated by lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. This integration significantly improves forecasting accuracy, providing more reliable insights into future mortality trends. The results have significant implications for public health planning and policymaking, offering a robust tool for forecasting demographic changes and their impact on healthcare systems. This integration advances mortality forecasting, leading to better public health outcomes. Universiti Teknologi MARA, Negeri Sembilan 2024-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/106004/1/106004.pdf Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob. (2024) Journal of Exploratory Mathematical Undergraduate Research (JEMUR) <https://ir.uitm.edu.my/view/publication/Journal_of_Exploratory_Mathematical_Undergraduate_Research_=28JEMUR=29/>, 2. ISSN 3030-5411
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic L Education (General)
QA Mathematics
spellingShingle L Education (General)
QA Mathematics
Abd Hamid, Hana Natasya
Khairul Hizam, Khairunnisa
Yaacob, Nurul Aityqah
Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob
description This research integrates the Lee-Carter (LC) model with neural network (NN) methods to enhance mortality forecasting. The LC model, widely used for demographic forecasting, has limitations in capturing complex and nonlinear mortality trends. To address these limitations, we incorporate NN methods, namely a multilayer feed-forward neural network (MFFNN), to identify patterns within mortality data. The study evaluates the performance of the LC and LC-NN models across five countries: Germany, Japan, Malaysia, South Korea, and the United States of America (USA). Findings indicate that the LC-NN model outperforms the LC model, as demonstrated by lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. This integration significantly improves forecasting accuracy, providing more reliable insights into future mortality trends. The results have significant implications for public health planning and policymaking, offering a robust tool for forecasting demographic changes and their impact on healthcare systems. This integration advances mortality forecasting, leading to better public health outcomes.
format Article
author Abd Hamid, Hana Natasya
Khairul Hizam, Khairunnisa
Yaacob, Nurul Aityqah
author_facet Abd Hamid, Hana Natasya
Khairul Hizam, Khairunnisa
Yaacob, Nurul Aityqah
author_sort Abd Hamid, Hana Natasya
title Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob
title_short Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob
title_full Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob
title_fullStr Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob
title_full_unstemmed Improving mortality forecasting: integrating the lee-carter model with neural networks / Hana Natasya Abd Hamid, Khairunnisa Khairul Hizam and Nurul Aityqah Yaacob
title_sort improving mortality forecasting: integrating the lee-carter model with neural networks / hana natasya abd hamid, khairunnisa khairul hizam and nurul aityqah yaacob
publisher Universiti Teknologi MARA, Negeri Sembilan
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/106004/1/106004.pdf
https://ir.uitm.edu.my/id/eprint/106004/
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score 13.244413

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