Ensemble Stacking Classifier for Cardiovascular Risk Prediction

Cardiovascular diseases (CVDs) pose significant challenges globally, necessitating prompt and accurate identification of individuals at risk to implement preventative measures effectively and optimize healthcare resources. This study proposes a novel approach to predict cardiovascular risk using an ensemble stacking classifier. The ensemble stacking classifier combines multiple base classifiers, including Logistic Regression, Decision Tree, Nearest Neighbors, and advanced machine learning algorithms. Each base classifier is designed to capture different facets of cardiovascular risk factors. By integrating various models, the ensemble stacking classifier aims to exploit their complementary strengths, effectively mitigating individual model weaknesses and enhancing overall predictive accuracy. This approach offers a comprehensive and robust solution for predicting cardiovascular risk, contributing to improved patient outcomes and healthcare resource utilization.