Comparison of Machine Learning Performance with TIMI and GRACE Score for Cardiovascular Risk Prediction in Acute Coronary Syndrome: Meta-Analysis

Acute Coronary Syndrome (ACS) risk stratification relies on TIMI and GRACE scores, which lack accuracy for individual-level predictions. Machine Learning (ML) offers promising alternatives but faces challenges in interpretability and clinical adoption. This meta-analysis compares ML models (DNN, XGBoost, Random Forest, GBDT, SVM) with TIMI/GRACE scores in predicting cardiovascular events, while addressing implementation barriers. Following PRISMA guidelines, we analyzed 50 studies (1,592,034 patients) from PubMed, Scopus, and Web of Science (2015–2025). Performance metrics (AUC, sensitivity, specificity) were pooled using random-effects models, and publication bias was assessed via funnel plots. ML models significantly outperformed conventional scores, with Random Forest (AUC=0.99), XGBoost (AUC=0.98), and DNN (sensitivity=99%) demonstrating superior discrimination. However, heterogeneity in validation (e.g., Asian vs. European cohorts) and "black-box" limitations were identified. The study advocates for explainable AI, multi-center validation, and clinician training to facilitate ML integration into Electronic Health Records (EHRs). These steps could establish ML as the new standard in ACS care, improving outcomes while reducing healthcare costs.