An artificial intelligent approach on longevity modeling

Longevity and life science are active topics in biomedicine and other research areas. Because human life is a complex multi-variant natural process, it is complicated yet important to extract expert knowledge that can describe the interactions among different factors and influence of the factors on human life. In this paper, expert knowledge is extracted into a longevity model by using artificial intelligent (AT) techniques: fuzzy logic that reflects prior expert knowledge is used in the preprocessing of bio-medical data; multiple classifier network and decision level data fusion are then applied to improve the modeling accuracy. The test results show that the proposed model is able to identify individuals who belong to longevity group with 93 percent accuracy. This research creates a new approach to explore the cause of human longevity based on comprehensive medical data rather than just from one medical subject. Therefore this methodology accords with the nature of human life more closely