Grey Wolf Marking-Optimized Deep Learning Framework for Cardiovascular Event Prediction in Peritoneal Dialysis Patients

Background: Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality among patients undergoing peritoneal dialysis (PD). Despite advances in clinical management, accurate prediction of cardiovascular events in patients receiving PD continues to be a major challenge due to the complexity and heterogeneity of patient data. This study proposes a novel Pro-PD diagnostic model to enhance the prediction of cardiovascular events, including myocardial dysfunction, stroke, and heart attack. Methods: The model integrates deep learning (DL) with the Grey Wolf Marking (GWM) optimization technique and incorporates a dual-mode authentication and decision-support mechanism governed by GWM to optimize feature selection and classification performance. It leverages multidimensional datasets comprising demographic characteristics, clinical indicators, and dialysis-specific parameters to extract complex patterns associated with CVD risk. Results: Experimental evaluation demonstrates that the proposed approach achieves an accuracy of 95.62% in identifying selective pattern attributes and classifying PD patients into CVD risk categories. Conclusions: The proposed Pro-PD framework demonstrated promising predictive performance, achieving 95.62% training accuracy and 92.64% validation accuracy for the local model under an 80% training–20% validation split. The global feature-mapping model achieved an AUC (area under ROC curve) of 92.41%.