Integrating Cardiorenal Biomarkers and Imaging with Machine Learning to Predict Coronary Events in Stage 3–5 Chronic Kidney Disease Patients in Pakistan

Abstract Background: Chronic kidney disease (CKD) is a major risk factor for cardiovascular events, yet predicting coronary events in CKD patients remains challenging. The study seeks to create a prediction model that combines cardiorenal biomarkers and imaging with Machine Learning (ML) to enhance early diagnosis in individuals with stage 3–5 CKD. Objective: To create and validate a ML-based predictive model combining biomarkers (KIM-1, BNP) and imaging data (echocardiography, coronary angiography) to predict coronary events in CKD patients. Methodology: A total of 250 patients with stage 3–5 CKD were included in a retrospective cohort. ML models, such as Random Forest, Support Vector Machines and Gradient Boosting, were built based on clinical features, biomarkers and imaging signs. The diagnostic value of model was calculated with the accuracy, sensitivity, specificity and area under receiver operating characteristic curve. Results: The Random Forest model achieved the highest AUC of 0.82, with an accuracy of 85.2%, sensitivity of 84.5%, and specificity of 85.8%. The inclusion of biomarkers and imaging data significantly enhanced the prediction of coronary events in CKD patients. Conclusion: The integrated ML model demonstrates strong predictive capability for coronary events in CKD patients, giving medical professionals a useful tool for making decisions. This model could facilitate earlier interventions, reduce adverse cardiovascular outcomes, and guide personalized treatment strategies, particularly in CKD patients at high risk of cardiovascular events.