Explainable AI and transformer models: Unraveling the nutritional influences on Alzheimer's disease mortality.

ABSTRACT: This pioneering study introduces the use of transformer-based machine learning models and explainable AI approaches to explore the impact of nutrition on Alzheimer's disease (AD) mortality. Using data from the Third National Health and Nutrition Examination Survey (Nhanes iii 1988 to 1994) and the NHANES III Mortality-Linked File (2019) databases, we investigate the intricate relationship between various nutritional factors and AD mortality. Our approach features a novel application of transformer models, which are then benchmarked against established methods like random forests and support vector machines. This comparison not only underscores the strengths of transformer models in handling complex medical datasets but also highlights their potential for providing deeper insights into dise

SUBMITTER: Liu Z 

PROVIDER: S-EPMC11290104 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature