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Temporal Clustering of the Causes of Death for Mortality Modelling

Author

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  • Nicholas Bett

    (African Center of Excellence in Data Science (ACEDS), College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, Rwanda
    Department of Mathematics, Physics, and Computing, School of Science and Aerospace Studies, Moi University, Eldoret P.O. Box 3900, Kenya)

  • Juma Kasozi

    (Department of Mathematics, College of Natural Sciences, Makerere University, Kampala P.O. Box 7062, Uganda)

  • Daniel Ruturwa

    (Department of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, Rwanda)

Abstract

Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling.

Suggested Citation

  • Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2022. "Temporal Clustering of the Causes of Death for Mortality Modelling," Risks, MDPI, vol. 10(5), pages 1-34, May.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:5:p:99-:d:809372
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    References listed on IDEAS

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    Cited by:

    1. Nelson Kemboi Yego & Juma Kasozi & Joseph Nkurunziza, 2023. "Optimizing Pension Participation in Kenya through Predictive Modeling: A Comparative Analysis of Tree-Based Machine Learning Algorithms and Logistic Regression Classifier," Risks, MDPI, vol. 11(4), pages 1-21, April.
    2. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2023. "Dependency Modeling Approach of Cause-Related Mortality and Longevity Risks: HIV/AIDS," Risks, MDPI, vol. 11(2), pages 1-18, February.
    3. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.

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