Paper Title
Ensemble Machine Learning Models for Cardiovascular Disease Prediction

Abstract
Cardiovascular Diseases (CVD) are a leading cause of death globally, creating a demand for predictive tools that enable early diagnosis and improved patient care. Machine Learning algorithms are widely used methods in predicting the CVD risk. Conventionally, numerous algorithms have been employed to predict the risk of cardiovascular disease (CVD). However, to improve the accuracy of predictions, ensemble machine learning techniques are now being used. By integrating various data sources, ensemble models support more comprehensive risk assessments than traditional methods. In this review paper, we discuss on usage of ensemble machine learning techniques such as Random Forest, Gradient Boosting, and Stacking, which combines multiple machine learning models. A comprehensive comparison is provided, highlighting their advantages, limitations, and diagnostic performance. Additionally, this study incorporates a flowchart methodology. Case studies are included to highlight the practical application of these models in real-world healthcare scenarios. Despite their effectiveness, ensemble models face challenges, particularly in interpretability and seamless integration into clinical practices. Techniques from Explainable AI (XAI) can address these limitations, fostering trust among clinicians and enabling user-friendly applications in healthcare workflows. This paper concludes by highlighting the importance of collaboration between AI researchers and medical professionals to address current gaps and develop more scalable, interpretable, and ethically aligned solutions for CVD diagnostics and treatment. Keywords - Cardiovascular Disease Prediction, Machine Learning, Ensemble Learning, Random Forest, Gradient Boosting, Stacking