Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI Approach

TL;DR

This study applies machine learning and explainable AI to analyze how nutritional factors influence Alzheimer's disease mortality, identifying key nutrients like vitamin B12 and hemoglobin as significant predictors.

Contribution

It introduces an explainable AI framework using random forests and SHAP to uncover nutritional impacts on Alzheimer's mortality from NHANES data.

Findings

Vitamin B12 significantly affects AD mortality

Glycated hemoglobin is a key predictor

Random forest outperforms other models in prediction accuracy

Abstract

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.