Predicting Clinical Outcomes in Membranous Nephropathy Using Machine Learning

Objective This study aimed to develop and compare machine learning models for predicting clinical outcomes—Complete Remission (CR), partial relief (PR), and Relapse—in patients with idiopathic membranous nephropathy (IMN). We specifically evaluated whether incorporating temporal summary features derived from longitudinal laboratory data could enhance predictive performance beyond baseline measurements. Methods We conducted a retrospective cohort study of 336 IMN patients from Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou (2021–2024). Predictors included demographic characteristics, lifestyle factors (smoking, alcohol consumption), and laboratory parameters. For each laboratory variable, we constructed a comprehensive feature set comprising baseline values, mean, maximum, minimum, and change from 0–12 weeks. The dataset was partitioned into training and test sets, and we compared three machine learning approaches: Random Forest (RF), XGBoost, and Multinomial Logistic Regression (MLR), using 5-fold cross-validation for hyperparameter tuning. Results The tree-based ensemble models demonstrated superior predictive capability. XGBoost achieved the highest overall accuracy (0.754), followed closely by random forest. Multinomial Logistic Regression showed moderately lower performance. Critically, models utilizing the temporal summary feature set consistently outperformed those relying solely on baseline data across all algorithms. Conclusions Tree-based ensemble models, particularly XGBoost and Random Forest, effectively predict clinical outcomes in idiopathic membranous nephropathy when incorporating temporal feature engineering from longitudinal laboratory data. XGBoost demonstrated superior performance in relapse prediction (AUC = 0.948), while Random Forest achieved balanced multiclass performance (Macro-AUC = 0.935). These approaches offer promising avenues for risk stratification and personalized treatment planning in IMN management, warranting further validation in multi-center settings.