Hotelling T2 Control Chart for Detecting Changes in Mortality Models Based on Machine-Learning Decision Tree

Suryo Adi Rakhmawan, M. Hafidz Omar, Muhammad Riaz, Nasir Abbas*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Mortality modelling is a practical method for the government and various fields to obtain a picture of mortality up to a specific age for a particular year. However, some information on the phenomenon may remain in the residual vector and be unrevealed from the models. We handle this issue by employing a multivariate control chart to discover substantial cohort changes in mortality behavior that the models still need to address. The Hotelling T2 control chart is applied to the externally studentized deviance model, which is already optimized using a machine-learning decision tree. This study shows a mortality model with the lowest MSE, MAPE, and deviance, by accomplishing simulations in various countries. In addition, the model that is more sensitive in detecting signals on the control chart is singled out so that we can perform a decomposition to determine the attributes of death in the specific outlying age group in a particular year. The case study in the decomposition uses data from the country Saudi Arabia. The overall results demonstrate that our method of processing and producing mortality models with machine learning can be a solution for developing countries or countries with limited mortality data to produce accurate predictions through monitoring control charts.

Original languageEnglish
Article number566
Issue number3
StatePublished - Feb 2023

Bibliographical note

Funding Information:
This work was supported by the Deanship of Research Oversight and Coordination (DROC) at the King Fahd University of Petroleum and Minerals (KFUPM) under project # SB191042.

Publisher Copyright:
© 2023 by the authors.


  • Hotelling T
  • Lee–Carter model
  • control chart
  • machine learning
  • mortality modelling

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Mathematics (all)
  • Engineering (miscellaneous)


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