PDF(Pubmed)
Abstract：
UNASSIGNED: The relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex steroid hormones, and DA to gout identification.
UNASSIGNED: The dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process.
UNASSIGNED: An initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output.
UNASSIGNED: The interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.
UNASSIGNED: The dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process.
UNASSIGNED: An initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output.
UNASSIGNED: The interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.
摘要：
■全身炎症指数（SII），性类固醇激素,膳食抗氧化剂（DA），痛风尚未确定。我们的目标是开发一种可靠且可解释的机器学习（ML）模型，该模型将SII链接在一起，性类固醇激素,和DA到痛风鉴定。
■我们用来研究SII之间关系的数据集，性类固醇激素,DA,痛风来自国家健康和营养检查调查（NHANES）。开发了六个ML模型来通过SII识别痛风，性类固醇激素,和DA。总结了每个模型的七个性能判别特征，选择整体性能最佳的极限梯度提升(XGBoost)模型来识别痛风。我们使用Shapley加法扩张（SHAP）方法来解释XGBoost模型及其决策过程。
■对20,146名参与者进行的初步调查导致8,550名参与者被纳入研究。选择与SII相关的性能最佳的XGBoost模型，性类固醇激素,与DA鉴别痛风(男性:AUC:0.795,95%CI:0.746-0.843,准确率:98.7%;女性:AUC:0.822,95%CI:0.754-0.883,准确率:99.2%)。在男性群体中，SHAP值显示叶黄素+玉米黄质(LZ)的特征值较低,维生素C(VitC),番茄红素,锌，总睾酮（TT），维生素E(VIE),和维生素A（VitA），对模型产出的正向影响越大。在女性群体中，SHAP值显示E2、锌、番茄红素,LZ,TT,硒对模型输出有较大的正向影响。
■可解释的XGBoost模型证明了准确性，效率,以及识别SII之间关联的鲁棒性，性类固醇激素,DA,参与者的痛风。男性TT降低和女性E2降低可能与痛风有关。增加DA摄入量和减少SII可能会降低痛风的潜在风险。
■我们用来研究SII之间关系的数据集，性类固醇激素,DA,痛风来自国家健康和营养检查调查（NHANES）。开发了六个ML模型来通过SII识别痛风，性类固醇激素,和DA。总结了每个模型的七个性能判别特征，选择整体性能最佳的极限梯度提升(XGBoost)模型来识别痛风。我们使用Shapley加法扩张（SHAP）方法来解释XGBoost模型及其决策过程。
■对20,146名参与者进行的初步调查导致8,550名参与者被纳入研究。选择与SII相关的性能最佳的XGBoost模型，性类固醇激素,与DA鉴别痛风(男性:AUC:0.795,95%CI:0.746-0.843,准确率:98.7%;女性:AUC:0.822,95%CI:0.754-0.883,准确率:99.2%)。在男性群体中，SHAP值显示叶黄素+玉米黄质(LZ)的特征值较低,维生素C(VitC),番茄红素,锌，总睾酮（TT），维生素E(VIE),和维生素A（VitA），对模型产出的正向影响越大。在女性群体中，SHAP值显示E2、锌、番茄红素,LZ,TT,硒对模型输出有较大的正向影响。
■可解释的XGBoost模型证明了准确性，效率,以及识别SII之间关联的鲁棒性，性类固醇激素,DA,参与者的痛风。男性TT降低和女性E2降低可能与痛风有关。增加DA摄入量和减少SII可能会降低痛风的潜在风险。
