Hybrid Learning Model for Cardiovascular Risk Assessment

Cardiovascular diseases are a global health concern. This study uses a combined machine learning approach to predict heart diseases - it joins Random Forest in addition to Linear Regression. The heart's workings are complex, and this research works to improve prediction accuracy. It does not set up the model structure beforehand. The framework trains on old patient data - this data includes age, cholesterol, blood pressure along with how people live. The Random Forest handles complicated connections, and Linear Regression helps explain them. The study looks how many points must be included - it shows what influences heart disease outcomes. This tells effective knowledge for individual healthcare programs. This hybrid methodology, without reliance on a predetermined model, keeps belief for enhancing quick prediction strategies and contributing to more effective interventions in cardiovascular health.