Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index.
Background: Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) in...
| Published in: | Nutrition & Metabolism Vol. 21; pp. 1 - 16 |
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| Main Authors: | , |
| Format: | research tables/charts Journal Article |
| Published: |
BioMed Central
5/14/2024
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| Online Access: | View this record in EBSCOhost |