Predicting Hyperkalemia in Patients with Chronic Kidney Disease Using the CatBoost Model and Multiple Interpretability Analyses

Hyperkalemia is a major complication of chronic kidney disease (CKD). However, owing to the absence of specific symptoms in its early stages, hyperkalemia frequently remains undiagnosed. This study aimed to develop a machine learning model for predicting the risk of early hyperkalemia in patients with CKD. By conducting a comparative analysis of six machine learning methods, CatBoost demonstrated superiority across various evaluation metrics. Further evaluation using confusion matrix and decision curve analysis (DCA) confirmed its high classification accuracy and substantial clinical utility. Meanwhile, through multiple interpretability analyses based on SHAP and Local Interpretable Model-agnostic Explanations (LIME) techniques, we precisely quantify the contributions and positive or negative effects of risk factors for hyperkalemia.