Machine learning prediction and SHAP interpretability analysis of heart failure risk in patients with hyperuricemia
Tian-Ming Gan1, Shi-Rong Wang1, Guan-Lian Mo1
1Department of Cardiology, The First Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
Insights
A simple model using six indicators can predict heart failure (HF) risk in patients with hyperuricemia. This tool aids early identification and intervention for cardiovascular health.
Area of Science:
- Cardiology
- Nephrology
- Public Health
Background:
- Cardiovascular disorders, especially heart failure (HF), pose a significant global health burden.
- Hyperuricemia is a recognized risk factor that elevates susceptibility to HF.
- Current HF risk prediction models are complex, hindering clinical application.
Purpose of the Study:
- To develop a simple, interpretable risk assessment model for HF in hyperuricemia patients.
- To identify accessible clinical indicators for routine use in risk stratification.
- To address the need for practical tools in managing cardiovascular risk.
Main Methods:
- Utilized NHANES data (2005-March 2020) including 1,603 adults with hyperuricemia.
- Applied various machine learning models (SVM, Random Forest, Logistic Regression, XGBoost) for prediction.
- Evaluated model performance using accuracy, sensitivity, F1-score, and ROC AUC; employed SHAP for feature importance.
Main Results:
- The Support Vector Machine (SVM) model demonstrated superior performance.
- Key predictors identified: chronic kidney disease, coronary heart disease, hypertension, serum potassium, serum osmolality, and sedentary time.
- These six indicators showed significant predictive power for HF in the hyperuricemia cohort.
Conclusions:
- A straightforward, interpretable tool for HF risk stratification in hyperuricemia patients is proposed.
- The model integrates six easily obtainable indicators for clinical utility.
- Further validation is recommended, but the model shows potential for early HF detection and intervention.
Aims:
Cardiovascular disorders, particularly heart failure (HF), are a critical global health challenge. Hyperuricemia, a key cardiovascular risk factor, significantly increases HF susceptibility. Existing HF risk prediction tools are often cumbersome, relying on extensive clinical parameters and tests, limiting their practical use. Therefore, there is an urgent need for a simple, interpretable model to assess HF risk in hyperuricemia patients.
Methods And Results:
Using 2005-March 2020 NHANES data (85,750 participants), 1,603 adults (≥18 years) with confirmed hyperuricemia were included. Accessible multidimensional indicators were selected for routine clinical use. Multiple machine learning models (Random Forest, Logistic Regression, XGBoost, SVM, etc.) were applied, with performance evaluated via accuracy, sensitivity, F1-score, and ROC AUC, etc. SHAP values analyzed feature importance for the best model. The SVM model showed the best overall performance, with chronic kidney disease, coronary heart disease, hypertension, serum potassium, serum osmolality, and sedentary time emerging as the top predictors. These six indicators demonstrated strong predictive power for HF in hyperuricemia patients, highlighting their clinical relevance.
Conclusion:
Integration of these six readily available indicators provides a simple, interpretable tool for HF risk stratification in hyperuricemia patients. While further longitudinal and multicenter validation is needed, the model shows promise for early identification and targeted intervention in clinical practice.
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