Ensemble Heartguard: Integrating Svm And Random Forest For Robust Heart Disease Prediction

Abstract

Cardiovascular diseases (CVDs) are a major cause of death worldwide, accounting for 31% of all fatalities each year. Effective intervention requires early detection. A revolutionary path forward in cardiovascular treatment is presented by the combination of medical research and machine learning (ML). Machine learning algorithms provide detailed insights into treatment outcomes and risk factors by analyzing a variety of datasets, including genetic, lifestyle, and imaging data. Accurate classification models facilitate early detection, which permits customized prophylactic actions. Early intervention is made easier by predictive models that take physiological and behavioral factors into account. Data scientists, doctors, and regulators must work together to address issues like data privacy and model interpretability. This methodology emphasizes data quality and ongoing monitoring, combines cutting-edge ML algorithms for rapid and accurate CVD diagnosis, and lays the groundwork for future improvements.