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Malaysian Journal of Computing (MJoC)

Corresponding Author

Suraya Fadilah Ramli ([email protected])

Abstract

Over the last several decades, lung cancer remains one of the most frequent causes of cancer mortality across the world with significant implications for public health planning. Accurate forecast of the mortality rate and life expectancy is necessary for guiding health care resources. This research aims to analyze the historical trend in lung cancer mortality rates, evaluate and compare the accuracy of the three mortality forecasting models; Lee Carter, Age Period Cohort (APC) and Cairns-Blake-Dowd (CBD), forecast lung cancer mortality rate from 2021 to 2031 and compute life expectancy at birth. The lung cancer mortality data for Malaysia covering from 2000-2020 year were extracted from Institute for health Metrics and Evaluation (IHME). In order to fit and test the models, data were split into training and testing sets. The model performing better was then considered to forecast up to 2031 and life expectancy were computed. The APC model demonstrated the best predictive performance, achieving the highest accuracy of Mean Absolute Percentage (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Based on APC model forecast, the baseline age effect demonstrated the expected rise in mortality rate with the age of the population. Beyond emphasizing the importance of strengthened public health efforts such as anti-smoking campaigns, early detection, and improve access to healthcare to enhance future longevity in Malaysia, these results also demonstrate the advantages of strong statistical modelling approaches in producing an accurate forecast in lung cancer mortality rates.

Publication Date

10-1-2026

Volume

11

Issue

2

Recommendation of Reviewers

yes

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