Ensemble Learning based Cardiovascular Disease Prediction Combining with Predictive Analytics & Risk Stratification

Abstract

Heart failure continues to be a significant contributor to illness and death on a global scale, highlighting the need for effective strategies in early detection and risk stratification. Therefore, the study presents an efficient approach to heart failure prediction by integrating predictive analytics with advanced risk stratification techniques. This study develops and evaluates a novel ensemble learning-based predictive model that combines machine learning algorithms with clinical and demographic data to enhance early diagnosis and risk assessment. The proposed model leverages multiple data sources, such as electronic health records and patient history, to identify critical predictors and generate actionable insights. This study demonstrates significant improvements in prediction accuracy compared to GBC, RF, LR, DTC, and MLP 4%, 4%, 6%, 5%, and 4%, respectively, with enhanced capability to identify high-risk individuals before the onset of severe symptoms. By integrating these predictive and stratification techniques, the study offers a robust framework for early intervention and personalized treatment, ultimately contributing to better management of heart failure and improved patient outcomes. These findings show the potential of combining advanced analytics with clinical expertise to advance heart failure prediction and management.