Heart Attack Analysis Using Ensemble Machine Learning

Abstract

Heart disease remains one of the leading causes of mortality worldwide, emphasizing the urgent need for accurate and timely diagnosis. While traditional diagnostic methods have proven effective, advancements in machine learning (ML) offer promising avenues for enhanced detection and prevention strategies. This paper presents a comprehensive review of existing ML-based approaches for heart disease detection, ranging from classical statistical methods to cutting-edge deep learning techniques. We begin by outlining the various risk factors associated with heart disease, including hypertension, cholesterol levels, and lifestyle choices. Subsequently, we delve into the evolution of ML in healthcare, highlighting its transformative impact on diagnostic accuracy and patient care. Leveraging large-scale datasets and feature engineering, traditional ML algorithms such as Support Vector Machines (SVM) and Random Forests have demonstrated notable success in identifying cardiac abnormalities. However, recent breakthroughs in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have revolutionized heart disease detection by extracting intricate patterns and temporal dependencies from raw data sources.