ArticleFrontiers in immunology2024
Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- A Preliminary Predictive Panel for Pre-Frailty Based on Serum Proteomic Biomarkers: A Two-Phase Cross-Sectional Study.Clinical interventions in aging · 2026Article
- Status, challenges, and prospects of artificial intelligence application in gout diagnosis and treatment, drug research and development, and disease monitoring.Frontiers in medicine · 2026Review
- A Dynamic Prognosis Model of Patients with Chronic Heart Failure: A Prospective Cohort Study Using Follow-Up Data and Recurrent Neural Networks.Vascular health and risk management · 2026Observational
- Interpretable and explainable predictive machine learning models for gastric retention before gastroscopy.Frontiers in medicine · 2026Article
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- A Machine Learning Model Integrating Systemic Immune-Inflammation Index for Predicting 28-Day Mortality in Geriatric Sepsis Secondary to Community-Acquired Bacterial Pneumonia.Clinical interventions in aging · 2026Article
- Interpretable analysis of social anxiety status and influencing factors among college students.Frontiers in psychiatry · 2026Article
- Identifying factors and predicting mental health issues in polypharmacy elderly using machine learning: a study based on the English longitudinal study of aging.Frontiers in psychology · 2026Article
- Interpretable analysis of smartphone addiction status and its associated factors among college students.Frontiers in psychiatry · 2026Article
- Evaluating the Oxidative Balance Score for Peripheral Artery Disease Risk: Integrating Epidemiologic Modeling and SHAP-Interpretable Machine Learning in NHANES.Food science & nutrition · 2025Article
- Association Between the Glucose-to-Lymphocyte Ratio and 28-Day Mortality in Patients with Mechanical Ventilation.Balkan medical journal · 2025Article
- Development of a risk prediction model for secondary infection in severe/critical COVID-19 patients.BMC infectious diseases · 2025Article
- Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.European journal of medical research · 2025Article
- Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer.Frontiers in cardiovascular medicine · 2025Article
- Bayesian Optimization-Enhanced Machine Learning for Osteosarcoma Risk Stratification Based on Sphingolipid Metabolism.Human mutation · 2025Article
- MHR Was Associated with Hyperuricemia Risk in Patients with Type 2 Diabetes Mellitus: The Mediating Effect of Body Mass Index.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Systemic immune-inflammatory biomarkers combined with the CRP-albumin-lymphocyte index predict surgical site infection following posterior lumbar spinal fusion: a retrospective study using machine learning.Frontiers in medicine · 2025Article
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Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
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