  This article uses machine learning (ML) and explainable artificial
intelligence (XAI) techniques to investigate the relationship between
nutritional status and mortality rates associated with Alzheimers disease (AD).
The Third National Health and Nutrition Examination Survey (NHANES III)
database is employed for analysis. The random forest model is selected as the
base model for XAI analysis, and the Shapley Additive Explanations (SHAP)
method is used to assess feature importance. The results highlight significant
nutritional factors such as serum vitamin B12 and glycated hemoglobin. The
study demonstrates the effectiveness of random forests in predicting AD
mortality compared to other diseases. This research provides insights into the
impact of nutrition on AD and contributes to a deeper understanding of disease
progression.
