HeartcareNet

Problem: Early cardiovascular disease risk evaluation in clinical settings requires analyzing complex, multi-modal diagnostic indicators. Primary care clinicians in high-throughput environments need rapid, interpretable screening aids that highlight critical risk factors without black-box opacity. Solution: HeartcareNet is an explainable clinical machine learning pipeline trained to analyze patient vital signs, biometric markers, and demographic parameters. The system generates calibrated risk scores and produces feature attribution breakdowns using SHAP values, giving clinicians immediate insight into which specific parameters contributed most significantly to the score. Key Engineering Highlights: • Multi-model ensemble evaluating clinical tabular metrics against validated benchmark datasets. • SHAP (SHapley Additive exPlanations) integration for transparent risk factor ranking. • Fast, lightweight REST API serving inference in under 50ms. • Intuitive practitioner dashboard displaying patient risk percentiles and telemetry.