Artificial Neural Network

Artificial Neural Network (ANN) is one of the important methods in artificial intelligence that is widely used for prediction, classification, and pattern recognition. ANN is inspired by the structure of the human nervous system, where interconnected neurons process information simultaneously to produce specific outputs. In statistics and actuarial science, ANN can be utilized to model complex nonlinear relationships that are difficult to explain using conventional statistical methods. Therefore, ANN has become increasingly popular in demographic and mortality studies, including the development and analysis of Complete Life Tables.

A Complete Life Table is a demographic table that describes the mortality and survival experience of a population at every exact age interval. The table usually contains important indicators such as probability of death, number of survivors, life expectancy, and person-years lived. Traditionally, complete life tables are constructed using classical actuarial and statistical approaches. However, mortality data often exhibit nonlinear patterns influenced by various socioeconomic, environmental, and health-related factors. In this context, ANN provides an alternative modeling framework capable of learning complex mortality patterns from historical data and producing more flexible and accurate predictions.

The application of ANN in Complete Life Table analysis offers several advantages, particularly in forecasting mortality trends and estimating life expectancy. ANN models can process large datasets and capture hidden relationships among demographic variables without requiring strict distributional assumptions. Furthermore, ANN can improve prediction accuracy compared with traditional parametric models when mortality patterns are highly dynamic and heterogeneous across populations. Consequently, the integration of ANN and life table analysis has significant potential in demographic research, public health planning, insurance studies, and population policy evaluation.