Paper Title
EARLY PREDICTION OF CARDIVASCULAR DISEASE USING ENSEMBLE MACHINE LEARNING TECHNIQUES

Abstract
Abstract - The most important organ in the human body is the heart, whose incorrect operation can lead to a variety of complications. The medical industry frequently sponsors campaigns to disseminate information, offer guidance, and discover a treatment for cardiovascular illnesses. The computing scholars are also doing their best to assist the healthcare industry by offering a variety of solutions utilizing cutting-edge technologies. Similar to that, this research proposed an ensemble machine learning framework to forecast a patient's likelihood of developing heart failure based on a variety of attributes. On a real-world dataset, we ran a number of tests using various machine learning algorithms to achieve this. In this research, individual machine learning algorithms like Random Forest, Decision Tree, and Logistics Regression were used as well as ensemble techniques. The findings indicated that, with an accuracy of 84.48%, the voting approach using all three algorithms outperformed. The results of this research may have a positive effect and provide medical professionals with guidance by highlighting the vital indicators that must be kept under close watch in order for them to make timely decisions. The findings of this research can aid the medical community in developing awareness and prevention campaigns to appropriately direct heart patients. Keywords - Cardiovascular Disease, Machine Learning Algorithms, Heart Failure Factors, Ensemble Techniques.