Cardiac Risk Stratification Model

Cardiac Risk Stratification Model

Preventive cardiology would benefit from early identification of individuals likely to develop coronary artery disease before symptomatic presentation. This predictive analytics project assembles a multivariate dataset encompassing serum lipid profiles, blood pressure measurements, diabetic status, smoking history, and genetic risk markers from longitudinal patient records. A gradient-boosted ensemble model learns non-linear interactions among these features to output a ten-year cardiovascular event probability. Rigorous cross-validation and temporal split testing ensure the model generalizes to future patient populations. Clinician-facing dashboards present risk scores alongside feature contribution breakdowns, enabling personalized lifestyle and pharmacological intervention recommendations aligned with established prevention guidelines.

Components

Python 3.8+
Scikit-learn
XGBoost/LightGBM
Pandas & NumPy
Flask Dashboard
Matplotlib/Plotly