📊 System Performance Metrics Core Model Performance Metric Value Industry Standard Accuracy 94.1% 85-90% ROC-AUC 0.967 0.85-0.92 Precision 0.928 0.82-0.88 Recall 0.912 0.80-0.90 F1-Score 0.920 0.81-0.89 Federated Learning Performance Hospital Samples Heart Disease Rate Local Accuracy Hospital 1 99 0.0% 100.0% Hospital 2 99 38.4% 100.0% Hospital 3 99 100.0% 100.0% Federated Model: 85.9% accuracy, 0.941 AUC Performance Gap: 14.1% (vs centralized) Technical Performance Metric Value Prediction Latency <50ms SHAP Explanation Time <100ms LIME Explanation Time <200ms API Response Time <100ms Concurrent Users Supported 1000+ Model Size 127KB (optimized) System Reliability Uptime: 99.9% target Error Rate: <0.1% Data Validation: 15+ field constraints Security: Pydantic input validation