Hybrid Learning Model for Cardiovascular Risk Assessment

Cardiovascular diseases constitute a significant global health concern, demanding accurate predictive tools for timely intervention. This study addresses this imperative by employing a hybrid machine learning approach, integrating Random Forest and Linear Regression techniques for forecasting heart diseases. The complexity of cardiovascular dynamics requires a nuanced methodology, and this research endeavors to enhance forecasting precision without predefining the model structure. Leveraging a diverse patient dataset encompassing age, cholesterol levels, blood pressure, and lifestyle factors, the hybrid framework is trained on historical data, leveraging Random Forest for intricate relationships and Linear Regression for interpretability. Through meticulous feature importance analysis, the study unveils key factors influencing heart disease prognosis, offering valuable insights for personalized healthcare initiatives. This hybrid methodology, without reliance on a predetermined model, holds promise for improving early prediction strategies and contributing to more effective interventions in cardiovascular health.