Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI Approach
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910460514664448 |
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| author | Liu, Ziming Liu, Longjian Heidel, Robert E. Zhao, Xiaopeng |
| author_facet | Liu, Ziming Liu, Longjian Heidel, Robert E. Zhao, Xiaopeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17502 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI Approach Liu, Ziming Liu, Longjian Heidel, Robert E. Zhao, Xiaopeng Machine Learning Artificial Intelligence 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. |
| title | Exploring Nutritional Impact on Alzheimer's Mortality: An Explainable AI Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.17502 |