Temporal Clustering of the Causes of Death for Mortality Modelling
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 mor...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/10/5/99 |
| _version_ | 1797495826506842112 |
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| author | Nicholas Bett Juma Kasozi Daniel Ruturwa |
| author_facet | Nicholas Bett Juma Kasozi Daniel Ruturwa |
| author_sort | Nicholas Bett |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:55:08Z |
| format | Article |
| id | doaj.art-93ab1fdeb131490b9f490317c08e231c |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-10T01:55:08Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-93ab1fdeb131490b9f490317c08e231c2023-11-23T12:57:54ZengMDPI AGRisks2227-90912022-05-011059910.3390/risks10050099Temporal Clustering of the Causes of Death for Mortality ModellingNicholas Bett0Juma Kasozi1Daniel Ruturwa2African Center of Excellence in Data Science (ACEDS), College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, RwandaDepartment of Mathematics, College of Natural Sciences, Makerere University, Kampala P.O. Box 7062, UgandaDepartment of Applied Statistics, College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, RwandaActuaries 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.https://www.mdpi.com/2227-9091/10/5/99unsupervised learningcause of deathinsurancehierarchical clusteringspatial modellingapplications of statistics |
| spellingShingle | Nicholas Bett Juma Kasozi Daniel Ruturwa Temporal Clustering of the Causes of Death for Mortality Modelling Risks unsupervised learning cause of death insurance hierarchical clustering spatial modelling applications of statistics |
| title | Temporal Clustering of the Causes of Death for Mortality Modelling |
| title_full | Temporal Clustering of the Causes of Death for Mortality Modelling |
| title_fullStr | Temporal Clustering of the Causes of Death for Mortality Modelling |
| title_full_unstemmed | Temporal Clustering of the Causes of Death for Mortality Modelling |
| title_short | Temporal Clustering of the Causes of Death for Mortality Modelling |
| title_sort | temporal clustering of the causes of death for mortality modelling |
| topic | unsupervised learning cause of death insurance hierarchical clustering spatial modelling applications of statistics |
| url | https://www.mdpi.com/2227-9091/10/5/99 |
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