Data Mining Techniques for Mortality at Advanced Age

This paper addresses issues and techniques for advanced age mortality study using data mining techniques, a new technology on the horizon with great actuarial potential. Data mining is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. Both expert opinion and information discovery techniques are integrated together to guide each step in the information discovery process. Seven factors were considered in this study and the influences of these factors on advanced-age mortality distribution were identified with exploratory data analysis and decision tree algorithm. Models to address their effects on advanced age mortality were built with logistic regression technique. These models will be derived for projecting advanced age mortality distribution.