A Study On a Machine Learning Based Classification Approach in Identifying Heart Disease Within E-Healthcare

Abstract

The heart, as the second most vital organ after the brain, is integral to maintaining bodily equilibrium, and disruptions to its function have profound health consequences. Heart disease, a leading global cause of mortality, often arises from cumulative daily physiological changes, emphasizing the importance of timely illness prediction. In healthcare, the fusion of data mining and machine learning, explored in this study using Support Vector Machine, Decision Tree, and Random Forest algorithms, addresses the challenges of diagnosing prevalent conditions like heart disease, particularly crucial in the field of cardiology. Our proposed machine learning-based approach for diagnosing cardiac disease employs a range of classification algorithms and advanced feature selection techniques, demonstrating superior accuracy in detecting heart diseases from extensive datasets of unprocessed medical images. This technological advancement holds the potential to significantly enhance patient care in various healthcare settings, showcasing the promising impact of artificial intelligence tools on improving the quality of life for billions worldwide.