Improving mortality forecasting using a hybrid of Lee–Carter and stacking ensemble model

Abstract

Mortality forecasting is a critical component in various fields, including public health, insurance, and pension planning, where accurate predictions are essential for informed decision-making. This study introduces an innovative hybrid approach that combines the classical Lee–Carter model with advanced machine learning techniques, particularly the stack ensemble model, to enhance the accuracy and efficiency of mortality forecasts. Through an extensive analysis of mortality data from Ghana, the hybrid model’s performance is assessed, showcasing its superiority over individual base models. The proposed hybrid Lee–Carter model with a stack ensemble emerges as a powerful tool for mortality forecasting based on the performance metrics utilized. Additionally, the study highlights the impact of incorporating additional base models within the stack ensemble framework to enhance predictive performance. Through this innovative approach, the study provides valuable insights into enhancing mortality prediction accuracy. By bridging classic mortality modeling with advanced machine learning, the hybrid model offers a powerful tool for policymakers, actuaries, and healthcare practitioners to inform decisions and plan for the future. The findings of this research pave the way for further advancements and improvements in mortality forecasting methodologies, thus contributing to the broader understanding and management of mortality risks in various sectors.