可解释的机器学习框架,用于预测与膳食纤维和甘油三酯-葡萄糖指数相关的痛风。

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-05-14 DOI:10.1186/s12986-024-00802-2
Shunshun Cao, Yangyang Hu
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引用次数: 0

摘要

背景:痛风预测对于制定个性化预防和治疗计划至关重要。我们的目标是利用SHAPLE Additive exPlanation(SHAP)开发一种高效且可解释的机器学习(ML)模型,将膳食纤维和甘油三酯-葡萄糖(TyG)指数联系起来,以预测痛风:利用美国国家健康与营养调查(NHANES)(2005-2018 年)的数据集研究膳食纤维,并利用 TyG 指数预测痛风。在评估了六种ML模型的性能并选择轻梯度提升机(LGBM)作为最佳算法后,我们对使用SHAP预测痛风的LGBM模型进行了解读,并揭示了该模型的决策过程:我们对 70,190 名参与者进行了初步调查,经过逐步排除,最终将 12,645 个病例纳入研究。选出了预测痛风与膳食纤维和 TyG 指数相关的最佳 LGBM 模型(ROC 曲线下面积 (AUC):0.823,95% 置信度):0.823,95% 置信区间 (CI):0.798-0.848,准确率:95.3%,布赖尔评分:0.077)。SHAP 值的特征重要性表明,年龄是影响模型输出结果的最重要特征,其次是尿酸(UA)。SHAP 值显示,较低的膳食纤维值对模型的正向预测有更明显的影响,而较高的 TyG 指数值对模型的正向预测有更明显的影响:结论:与膳食纤维和 TyG 指数相关的可解释 LGBM 模型在预测痛风方面表现出较高的准确性、效率和稳健性。增加膳食纤维摄入量和降低TyG指数有利于降低痛风的潜在风险。
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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) 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.

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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
期刊介绍: Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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