SR dataset for Machine learning models

This dataset was compiled as part of a systematic review and meta-analysis evaluating the performance of machine learning models in predicting acute kidney injury (AKI) following cardiac surgery. It includes extracted data from published studies, such as model types (e.g., support vector machines, ensemble methods, XGBoost), performance metrics (AUC, sensitivity, specificity), validation approaches, and study characteristics. The dataset supports reproducibility of the review and can be used for secondary analyses or to inform future research on machine learning applications in perioperative risk prediction.