Chronic Kidney Disease (CKD) is a prevalent and progressive condition that can lead to end-stage renal disease (ESRD) if left unmanaged. Accurate prediction of CKD progression, particularly in patients with CKD stages 3-5, is essential for early intervention and personalized treatment. This study utilized machine learning (ML) models to predict declines in estimated glomerular filtration rate (eGFR) over one year. The models, including LGBM and Random Forest, were trained on a large cohort of CKD patients from the Taipei Medical University Clinical Research Database (TMUCRD). LightGBM emerged as the top-performing model with AUC values of 0.76 and 0.82 for predicting 5% and 25% declines in eGFR, respectively. SHAP (Shapley Additive Explanations) analysis identified baseline eGFR, eGFR slope, and BUN as key predictive features. The results demonstrate the utility of ML in CKD management and highlight the importance of personalized prediction models for improving patient outcomes.
Duong et al. (Thu,) studied this question.