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...
| Publicado en: | Nutrition & Metabolism Vol. 21; pp. 1 - 16 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
5/14/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177311669&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177311669 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17437075 1CYX jtl: Nutrition & Metabolism issn: 17437075 maglogo: N pubinfo: dt: 5/14/2024 vid: 21 pid: 24147 pub: BioMed Central artinfo: ui: 177311669 177311669 177311669 10.1186/s12986-024-00802-2 177311669 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index. aug: au: Cao, Shunshun Hu, Yangyang affil: Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, 325000, Wenzhou, Zhejiang, China sug: subj: Machine Learning Conceptual Framework Gout Risk Factors Gout Prevention and Control Dietary Fiber Triglycerides Analysis Blood Glucose Analysis Prediction Models Human Surveys ROC Curve Confidence Intervals Age Factors Questionnaires Uric Acid Blood Cross Sectional Studies ab: 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) index to predict gout. Methods: Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005–2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. Results: An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798–0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. Conclusion: The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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